diff --git a/birdnet_analyzer/analyze/cli.py b/birdnet_analyzer/analyze/cli.py
index d786c38c4..1a794c0e3 100644
--- a/birdnet_analyzer/analyze/cli.py
+++ b/birdnet_analyzer/analyze/cli.py
@@ -43,4 +43,4 @@ def main():
     analyze_args.pop("use_perch")  # handled via model param
     analyze_args.pop("load_params")  # already applied as defaults
 
-    analyze(**analyze_args)
+    analyze(**analyze_args, save_params=True)
diff --git a/birdnet_analyzer/analyze/core.py b/birdnet_analyzer/analyze/core.py
index f6f090e15..59b359540 100644
--- a/birdnet_analyzer/analyze/core.py
+++ b/birdnet_analyzer/analyze/core.py
@@ -49,70 +49,96 @@ def analyze(
     split_tables: bool = False,
     strict_species_list: bool = False,
     save_params: bool = False,
-    show_progress: bool = False,
     _return_only=False,
 ):
     """
     Analyzes audio files for bird species detection using the BirdNET-Analyzer.
     Args:
-        audio_input (str): Path to the input directory or file containing audio data.
-        output (str | None, optional): Path to the output directory for results.
-            Defaults to None.
-        min_conf (float, optional): Minimum confidence threshold for detections.
+        audio_input (str): Path to an audio file, or to a folder that is searched
+            recursively for audio files.
+        output (str | None, optional): Folder for the result files. Defaults to None,
+            which is the folder of an input file or the input folder itself.
+        model (str, optional): "birdnet" or "perch". Defaults to "birdnet".
+        birdnet (Literal["2.4", "3.0"], optional): BirdNET version used when
+            ``model`` is "birdnet". Defaults to "3.0".
+        min_conf (float, optional): Minimum confidence of a detection, in [0, 1).
             Defaults to 0.25.
-        classifier (str | None, optional): Path to a custom classifier file.
-            Defaults to None.
-        lat (float | None, optional): Latitude for location-based filtering.
-            Defaults to None.
-        lon (float | None, optional): Longitude for location-based filtering.
-            Defaults to None.
-        week (int, optional): Week of the year for seasonal filtering. Defaults to -1.
-        slist (str | None, optional): Path to a species list file for filtering.
+        classifier (str | None, optional): Path to a custom classifier (.tflite),
+            loaded instead of the BirdNET model; its BirdNET version is read from the
+            file. Cannot be combined with ``model`` "perch". Defaults to None.
+        cc_species_list (str | None, optional): Labels file of ``classifier``.
+            Defaults to None, which is the classifier path with ".tflite" replaced
+            by "_Labels.txt".
+        lat (float | None, optional): Latitude of the recording location. Together
+            with ``lon``, the detections are limited to the species the geo model
+            predicts there. Defaults to None.
+        lon (float | None, optional): Longitude of the recording location.
             Defaults to None.
+        week (int | None, optional): Week of the year (1-48) for the geo model.
+            Defaults to None, the year-round list.
+        slist (str | Path | Collection[str] | None, optional): Species to limit the
+            detections to: a species list file, a folder holding "species_list.txt",
+            or the labels themselves. Defaults to None.
         sensitivity (float, optional): Sensitivity of the detection algorithm;
             applies to every BirdNET model and custom classifier, ignored (with a
             warning) for Perch. Defaults to 1.0.
-        overlap (float, optional): Overlap between analysis windows in seconds.
+        overlap (float, optional): Overlap of consecutive segments in seconds.
             Defaults to 0.
-        fmin (int, optional): Minimum frequency for analysis in Hz. Defaults to 0.
-        fmax (int, optional): Maximum frequency for analysis in Hz. Defaults to 15000.
-        audio_speed (float, optional): Speed factor for audio playback during analysis.
-            Defaults to 1.0.
-        batch_size (int, optional): Batch size for processing. Defaults to 1.
-        rtype (Literal["table", "audacity", "kaleidoscope", "csv", "parquet"] |
-        List[Literal["table", "audacity", "kaleidoscope", "csv", "parquet"]], optional):
-            Output format(s) for results. Defaults to "table".
-        sf_thresh (float, optional): Threshold for species filtering. Defaults to 0.03.
-        top_n (int | None, optional): Limit the number of top detections per file.
-            Defaults to None.
-        merge_consecutive (int, optional): Merge consecutive detections within this time
-            window in seconds. Defaults to 1.
-        threads (int, optional): Number of CPU threads to use for analysis.
-            Defaults to 8.
-        locale (str, optional): Locale for species names and output. Defaults to "en".
-        additional_columns (list[str] | None, optional): Additional columns to include
-            in the output. Defaults to None.
-        use_perch (bool, optional): Whether to use the Perch model for analysis.
-            Defaults to False.
-        split_tables (bool, optional): Whether to split output tables by input files.
-            Defaults to False.
+        fmin (int, optional): Lower bandpass frequency in Hz. Defaults to 0.
+        fmax (int, optional): Upper bandpass frequency in Hz. Defaults to 15000.
+        audio_speed (float, optional): Speed factor applied to the audio before the
+            analysis. Defaults to 1.0.
+        batch_size (int, optional): Segments per inference batch; a classifier
+            trained on BirdNET 3.0 always takes 1. Defaults to 1.
+        n_workers (int | None, optional): Number of inference worker processes.
+            Defaults to None, one per physical CPU core.
+        n_producers (int, optional): Number of processes reading the audio files.
+            Defaults to 1.
+        rtype (str | list[str], optional): Result file format(s): "table" (Raven
+            selection table), "audacity", "kaleidoscope", "csv" or "parquet".
+            Defaults to "table".
+        sf_thresh (float, optional): Minimum occurrence frequency of a species in the
+            geo model's prediction. Defaults to 0.03.
+        top_n (int | None, optional): Keep at most this many detections per segment,
+            the highest-scoring ones; ``min_conf`` still applies. Defaults to None.
+        merge_consecutive (int, optional): Maximum number of consecutive detections
+            of a species merged into one; 0 or 1 disables merging. Defaults to 1.
+        locale (str, optional): Language of the species names of a BirdNET model;
+            one its version lacks falls back to "en_us" with a warning.
+            Defaults to "en_us".
+        additional_columns (list[str] | None, optional): Extra columns of the "csv"
+            and "parquet" results, any of "lat", "lon", "week", "overlap",
+            "sensitivity", "min_conf", "species_list" and "model". Defaults to None.
+        on_update (Callable[[AcousticProgressStats], None] | None, optional): Called
+            with the progress of the inference. Defaults to None.
+        split_tables (bool, optional): Write the results of each input file to files
+            of their own. Defaults to False.
         strict_species_list (bool, optional): If True, raise when a species in ``slist``
             is not in the model. If False (default), such species are matched to the
             model by scientific/common name where possible and any that remain unknown
-            are skipped with a warning. Only affects a user-provided ``slist``.
+            are skipped with a warning. Only affects an ``slist`` file.
+        save_params (bool, optional): Write the parameters to
+            "birdnet.analyze-params.csv" in ``output``. Defaults to False.
+        _return_only (bool, optional): Only return the predictions, without writing
+            result files or a resume journal. Defaults to False.
     Returns:
-        None
+        The predictions, a birdnet ``AcousticFilePredictionResult``. A resumed run
+        returns those of the files it analyzed, or None if none were left.
     Raises:
-        ValueError: If input path is invalid or required parameters are missing.
+        ValueError: If only one of ``lat`` and ``lon`` is given, they are combined
+            with ``slist``, ``classifier`` is combined with ``model`` "perch",
+            ``lat``, ``lon``, ``week`` or ``min_conf`` is out of range, ``model`` is
+            unknown, ``audio_input`` does not exist or holds no supported audio file,
+            or none of the species in an ``slist`` file (with
+            ``strict_species_list``, not all of them) are in the model.
+        FileNotFoundError: If the ``slist`` file or ``classifier`` does not exist.
+        RuntimeError: If the resume state in ``output`` is incomplete, or the
+            analysis is cancelled or one of its processes dies.
     Notes:
-        - The function ensures the BirdNET model is available before analysis.
-        - Analysis parameters are saved to a file in the output directory.
-        - Directory analyses are resumable: per-file results are journaled to
-          ``<output>/.birdnet-resume/`` as they complete, an interrupted run
-          picks up where it left off (if the analysis parameters match), and
-          the journal is removed after outputs are written. When resuming a
-          run that had already completed every file, no new inference happens
-          and ``None`` is returned instead of a prediction result.
+        Directory analyses are resumable: per-file results are journaled to
+        ``<output>/.birdnet-resume/`` as they complete, an interrupted run picks up
+        where it left off (if the analysis parameters match), and the journal is
+        removed after the outputs are written.
     """
     import birdnet_analyzer.config as cfg
     from birdnet_analyzer.analyze.resume import ResumeJournal, RunMetadata
@@ -124,6 +150,14 @@ def analyze(
     )
     from birdnet_analyzer.utils import save_params_file
 
+    if classifier and model == "perch":
+        raise ValueError("Select either Perch or a custom classifier, not both.")
+
+    if (lat is None) != (lon is None):
+        raise ValueError(
+            "Set both lat and lon to limit the species to a location, or neither."
+        )
+
     # Settled before the params file, result columns and resume fingerprint see it.
     sensitivity = effective_sensitivity(sensitivity, model)
     validate_min_conf(min_conf)
diff --git a/birdnet_analyzer/cli.py b/birdnet_analyzer/cli.py
index e66fbe988..ff24e70d6 100644
--- a/birdnet_analyzer/cli.py
+++ b/birdnet_analyzer/cli.py
@@ -12,9 +12,15 @@
 from birdnet_analyzer.config import (
     ALL_MODEL_LANGUAGES,
     AUTOTUNE_METRICS,
+    CROP_MODES,
     DEFAULT_ACOUSTIC_MODEL_VERSION,
     GEO_MODEL_LANGUAGES,
+    RESULT_TYPES,
+    SAMPLE_CROP_MODES,
+    SCORE_FUNCTIONS,
     TRAINED_MODEL_OUTPUT_FORMATS,
+    TRAINED_MODEL_SAVE_MODES,
+    UPSAMPLING_MODES,
 )
 from birdnet_analyzer.logs import setup_logging
 
@@ -467,9 +473,9 @@ def __call__(self, parser, namespace, values, option_string=None):
     parser.add_argument(
         "--rtype",
         default={"table"},
-        choices=["table", "audacity", "kaleidoscope", "csv", "parquet"],
+        choices=get_args(RESULT_TYPES),
         nargs="+",
-        help="Specifies output format. Values in `['table', 'audacity',  'kaleidoscope', 'csv', 'parquet']`.",
+        help="Specifies output format.",
         action=UniqueSetAction,
     )
     parser.add_argument(
@@ -482,7 +488,7 @@ def __call__(self, parser, namespace, values, option_string=None):
     parser.add_argument(
         "-c",
         "--classifier",
-        help="Path to custom trained classifier. If set, --lat, --lon and --locale are ignored.",
+        help="Path to custom trained classifier. Its species names come from its labels file, so --locale is ignored. --lat and --lon keep only its species that the geo model predicts there, or, with a warning, all of them if it predicts none.",
     )
     parser.add_argument(
         "--cc_species_list",
@@ -491,7 +497,7 @@ def __call__(self, parser, namespace, values, option_string=None):
     parser.add_argument(
         "--top_n",
         type=lambda a: max(1, int(a)),
-        help="Saves only the top N predictions for each segment independent of their score. Threshold will be ignored.",
+        help="Saves at most the top N predictions for each segment. --min_conf still applies.",
     )
     parser.add_argument(
         "--merge_consecutive",
@@ -579,14 +585,14 @@ def search_parser():
     parser.add_argument(
         "--score_function",
         default="cosine",
-        choices=["cosine", "euclidean", "dot"],
-        help="Scoring function to use. Choose 'cosine', 'euclidean' or 'dot'.",
+        choices=get_args(SCORE_FUNCTIONS),
+        help="Scoring function to use.",
     )
     parser.add_argument(
         "--crop_mode",
         default="center",
-        choices=["center", "first", "segments"],
-        help="Crop mode for the query sample. Can be 'center', 'first' or 'segments'.",
+        choices=get_args(CROP_MODES),
+        help="Crop mode for the query sample.",
     )
 
     parser.add_argument(
@@ -659,12 +665,12 @@ def segments_parser():
 
 
 def species_parser():
-    """Build the argument parser for retrieving a species list for a location."""
+    """Build the argument parser for retrieving a species list."""
     parser = argparse.ArgumentParser(
         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
         parents=[
             species_list_args(),
-            # The species list comes from the geo model, so only its languages apply.
+            # The geo model's languages, which the default acoustic model shares.
             locale_args(languages=GEO_MODEL_LANGUAGES),
             verbosity_args(),
         ],
@@ -711,8 +717,8 @@ def train_parser():
     parser.add_argument(
         "--crop_mode",
         default="center",
-        choices=["center", "first", "segments", "smart"],
-        help="Crop mode for training data. Can be 'center', 'first', 'segments' or 'smart'.",
+        choices=get_args(SAMPLE_CROP_MODES),
+        help="Crop mode for training data.",
     )
     parser.add_argument(
         "--birdnet",
@@ -793,7 +799,7 @@ def train_parser():
     parser.add_argument(
         "--upsampling_mode",
         default="repeat",
-        choices=["repeat", "linear", "mean", "smote"],
+        choices=get_args(UPSAMPLING_MODES),
         help="Upsampling mode.",
     )
     parser.add_argument(
@@ -801,12 +807,12 @@ def train_parser():
         nargs="+",
         default=["tflite"],
         choices=get_args(TRAINED_MODEL_OUTPUT_FORMATS),
-        help="Model output format(s). One or more of 'tflite', 'raven', 'detached'.",
+        help="Model output format(s).",
     )
     parser.add_argument(
         "--model_save_mode",
         default="replace",
-        choices=["replace", "append"],
+        choices=get_args(TRAINED_MODEL_SAVE_MODES),
         help="Model save mode. 'replace' will overwrite the original classification layer and 'append' will combine the original classification layer with the new one.",
     )
     parser.add_argument(
diff --git a/birdnet_analyzer/embeddings/core.py b/birdnet_analyzer/embeddings/core.py
index 3783ce1f3..204b033ee 100644
--- a/birdnet_analyzer/embeddings/core.py
+++ b/birdnet_analyzer/embeddings/core.py
@@ -40,7 +40,8 @@ def embeddings(
     Args:
         audio_input (str): Path to the input audio file or directory containing audio
             files.
-        database (str): Path to the database where embeddings will be stored.
+        database (str): Path to the database where embeddings will be stored. It is
+            created if it does not exist.
         overlap (float, optional): Overlap between consecutive audio segments in
             seconds. Defaults to 0.0.
         audio_speed (float, optional): Speed factor for audio processing.
@@ -53,15 +54,16 @@ def embeddings(
             batch. Defaults to 1.
         file_output (str | None, optional): Path to save the output embeddings. If None,
             embeddings are not saved to a file. Defaults to None.
-        n_workers (int | None, optional): Number of worker threads to use for
-            processing. Defaults to None.
-        n_producers (int, optional): Number of producer threads to use for processing.
-            Defaults to 1.
+        n_workers (int | None, optional): Number of worker processes to use for
+            processing. Defaults to None, one per physical CPU core.
+        n_producers (int, optional): Number of producer processes to use for
+            processing. Defaults to 1.
         on_update (Callable[[AcousticProgressStats], None] | None, optional): Callback
             function to report progress updates. Defaults to None.
     Raises:
-        FileNotFoundError: If the input path or database path does not exist.
-        ValueError: If any of the parameters are invalid.
+        ValueError: If ``audio_input`` does not exist or holds no supported audio
+            file, or if the database was created with a different ``fmin``, ``fmax``,
+            ``audio_speed`` or parent folder of ``audio_input``.
     Example:
         embeddings(
             "path/to/audio",
diff --git a/birdnet_analyzer/gui/analysis.py b/birdnet_analyzer/gui/analysis.py
index 1e73af545..1b2a4a206 100644
--- a/birdnet_analyzer/gui/analysis.py
+++ b/birdnet_analyzer/gui/analysis.py
@@ -16,9 +16,6 @@
 
 
 def on_progress(update: AcousticProgressStats, progress: gr.Progress):
-    if progress is None:
-        return
-
     if update.processed_segments and update.total_segments:
         progress(
             (update.processed_segments, update.total_segments),
@@ -65,35 +62,50 @@ def run_analysis(
     """Starts the analysis.
 
     Args:
-        input_path: Either a file or directory.
-        output_path: The output path for the result, if None the input_path is used
+        input_path: The audio file of the single file tab. If set, the predictions
+            are only returned, no result files are written.
+        output_path: The output folder; if None, the folder of the input.
+        use_top_n: Whether to keep the ``top_n`` highest-scoring detections per
+            segment instead of those above ``confidence``.
+        top_n: The number of detections kept per segment with ``use_top_n``.
         confidence: The selected minimum confidence.
         sensitivity: The selected sensitivity.
         overlap: The selected segment overlap.
         merge_consecutive: The number of consecutive segments to merge into one.
-        audio_speed: The selected audio speed.
+        audio_speed: The value of the audio speed slider.
         fmin: The selected minimum bandpass frequency.
         fmax: The selected maximum bandpass frequency.
         species_list_choice: The choice for the species list.
-        species_list_file: The selected custom species list file.
-        lat: The selected latitude.
-        lon: The selected longitude.
+        species_list_file: The species list file, used with the custom species list
+            choice.
+        lat: The latitude, used with the predicted species list choice.
+        lon: The longitude, used with the predicted species list choice.
         week: The selected week of the year.
         use_yearlong: Use yearlong instead of week.
         sf_thresh: The threshold for the predicted species list.
-        custom_classifier_file: Custom classifier to be used.
-        output_type: The type of result to be generated.
+        selected_model: The model choice: a BirdNET version, Perch or the custom
+            classifier.
+        custom_classifier_file: The classifier file, used with the custom classifier
+            choice.
+        output_types: The result type(s) to write.
         additional_columns: Additional columns to be added to the result.
-        output_filename: The filename for the combined output.
         locale: The translation to be used.
         batch_size: The number of samples in a batch.
-        n_producers: The number of producer threads to be used.
-        n_workers: The number of worker threads to be used.
-        input_dir: The input directory.
+        input_dir: The folder of the multi file tab, analyzed instead of
+            ``input_path``.
+        save_params: Whether to write the parameters file next to the results.
+        n_producers: The number of producer processes.
+        n_workers: The number of worker processes.
         progress: The gradio progress bar.
         split_tables: Whether to split the output into separate tables per input file.
+            Defaults to False.
+
+    Returns:
+        The predictions returned by the analysis.
+
+    Raises:
+        gr.Error: If the custom species list or custom classifier choice has no file.
     """
-    import birdnet_analyzer.gui.localization as loc
     from birdnet_analyzer.analyze import analyze
 
     if species_list_choice == gu.CUSTOM_SPECIES:
diff --git a/birdnet_analyzer/model.py b/birdnet_analyzer/model.py
index 65f283770..ab7c2a864 100644
--- a/birdnet_analyzer/model.py
+++ b/birdnet_analyzer/model.py
@@ -741,10 +741,12 @@ def save_raven_model(
         labels (list[str]): A list of labels associated with the classifier.
         mode (str, optional): The mode for saving the model. Can be either "replace" or
             "append". Defaults to "replace".
-        version: The BirdNET version the classifier was trained on.
-
-    Raises:
-        ValueError: If the mode is not "replace" or "append".
+        sig_fmin (int, optional): Lowest frequency in Hz written for every class to
+            "classes/classes.csv". Defaults to 0.
+        sig_fmax (int, optional): Highest frequency in Hz written for every class to
+            "classes/classes.csv". Defaults to 15000.
+        version: The BirdNET version the classifier was trained on. Defaults to the
+            newest version.
 
     Returns:
         None
diff --git a/birdnet_analyzer/model_utils.py b/birdnet_analyzer/model_utils.py
index 5d26da379..146515ca6 100644
--- a/birdnet_analyzer/model_utils.py
+++ b/birdnet_analyzer/model_utils.py
@@ -109,7 +109,8 @@ def _reconcile_species_list(
     For a user file, species the model does not know are reported - skipped with a
     warning, or raised when ``strict`` - because the user expects every entry to
     count. A geo-derived collection is broader than the acoustic model on purpose (it
-    can include non-birds), so its unmatched species are filtered out silently.
+    can include non-birds), so its unmatched species are filtered out silently, unless
+    none match: then a warning is logged.
     """
     from pathlib import Path
 
@@ -127,6 +128,12 @@ def _reconcile_species_list(
 
     matched, unmatched = match_species_to_model(requested, model_species)
 
+    if not is_user_file and not matched:
+        logger.warning(
+            "The location or species list names no species of the model, so the "
+            "analysis is not filtered by species."
+        )
+
     if is_user_file and unmatched:
         listing = "\n  ".join(sorted(unmatched))
         if not matched:
diff --git a/birdnet_analyzer/segments/core.py b/birdnet_analyzer/segments/core.py
index 8ebcd8a5f..a5d6b8561 100644
--- a/birdnet_analyzer/segments/core.py
+++ b/birdnet_analyzer/segments/core.py
@@ -52,18 +52,18 @@ def segments(
             confidence: Collects the segments with highest confidence.
             balanced: Collects segments with a balanced distribution of confidence
             levels.
+            Defaults to "random".
         n_bins (int, optional): Number of bins for confidence distribution when using
-        the "balanced" collection mode.
+            the "balanced" collection mode. Defaults to 10.
+        on_update (Callable[[tuple[int, int]], None] | None, optional): Called with
+            ``(i, n_files)`` as the audio files are processed. Defaults to None.
 
     Returns:
-        list[tuple[str, bool]]: A list of tuples containing the path to the extracted
-        segment and a boolean indicating whether the extraction was successful.
+        list[tuple[str, bool]]: A list of tuples containing the path to an audio file
+        and a boolean indicating whether its segments were extracted successfully.
     Notes:
         - The function uses multiprocessing for parallel processing if `threads` is
         greater than 1.
-        - On Windows, due to the lack of `fork()` support, configuration items are
-        passed to each process explicitly.
-        - It is recommended to use this function on Linux for better performance.
     """
     import concurrent.futures
 
diff --git a/birdnet_analyzer/segments/utils.py b/birdnet_analyzer/segments/utils.py
index b4730ad76..68be248b8 100644
--- a/birdnet_analyzer/segments/utils.py
+++ b/birdnet_analyzer/segments/utils.py
@@ -187,7 +187,7 @@ def parse_files(
             {"audio": "path/to/audio1.wav", "result": "path/to/result1.csv"},
             {"audio": "path/to/audio2.wav", "result": "path/to/result2.csv"}
         ]
-        segments = parseFiles(flist, max_segments=50)
+        segments = parse_files(flist, max_segments=50)
     """
     species_segments: dict[str, list] = {}
     is_combined_rfile = len(flist) == 1 and flist[0].get("isCombinedFile", False)
diff --git a/birdnet_analyzer/species/core.py b/birdnet_analyzer/species/core.py
index 348178743..853d34485 100644
--- a/birdnet_analyzer/species/core.py
+++ b/birdnet_analyzer/species/core.py
@@ -17,37 +17,47 @@ def species(
     locale: MODEL_LANGUAGES = "en_us",
 ):
     """
-    Retrieves and processes species data based on the provided parameters.
+    Writes a species list file, one label per line.
     Args:
-        output (str): The output directory or file path where the results will be
-                      stored.
-        lat (float | None, optional): Latitude of the location for species filtering.
-                                      Defaults to None (no filtering by location).
-        lon (float | None, optional): Longitude of the location for species filtering.
-                                      Defaults to None (no filtering by location).
-        week (int | None, optional): Week of the year for species filtering.
-                                     Defaults to None (no filtering by time).
-        sf_thresh (float, optional): Species frequency threshold for filtering.
-                                     Defaults to 0.03.
-        locale (MODEL_LANGUAGES, optional): Locale for species names.
+        output (str): Path of the list file, or a folder to write "species_list.txt"
+                      into.
+        lat (float | None, optional): Latitude of the location. Together with
+                                      ``lon``, the list holds the species the geo
+                                      model predicts there; with neither, every
+                                      species of the default acoustic model.
+                                      Defaults to None.
+        lon (float | None, optional): Longitude of the location. Defaults to None.
+        week (int | None, optional): Week of the year (1-48) for the geo model,
+                                     ignored without a location. Defaults to None,
+                                     the year-round list.
+        sf_thresh (float, optional): Minimum occurrence frequency of a species in the
+                                     geo model's prediction, ignored without a
+                                     location. Defaults to 0.03.
+        locale (MODEL_LANGUAGES, optional): Language of the species names.
                                             Defaults to "en_us".
     Raises:
-        FileNotFoundError: If the required model files are not found.
-        ValueError: If invalid parameters are provided.
+        ValueError: If only one of ``lat`` and ``lon`` is given, or ``lat``, ``lon``
+            or ``week`` is out of range.
     Notes:
-        This function ensures that the required model files exist before processing.
-        It delegates the main processing to the `run` function
-        from `birdnet_analyzer.species.utils`.
+        The geo model, or without a location the acoustic model, is downloaded on
+        first use.
     """
-    from birdnet_analyzer.species.utils import get_species_list
-
-    species_list = get_species_list(
-        lat=-1 if lat is None else lat,
-        lon=-1 if lon is None else lon,
-        week=week,
-        threshold=sf_thresh,
-        lang=locale,
-    )
+    if lat is None and lon is None:
+        from birdnet_analyzer.config import DEFAULT_ACOUSTIC_MODEL_VERSION
+        from birdnet_analyzer.model_utils import acoustic_species_list
+
+        species_list = acoustic_species_list(DEFAULT_ACOUSTIC_MODEL_VERSION, locale)
+    elif lat is None or lon is None:
+        raise ValueError(
+            "Set both lat and lon for the species list of a location, or neither for "
+            "every species of the model."
+        )
+    else:
+        from birdnet_analyzer.species.utils import get_species_list
+
+        species_list = get_species_list(
+            lat=lat, lon=lon, week=week, threshold=sf_thresh, lang=locale
+        )
 
     if os.path.isdir(output):
         output = os.path.join(output, "species_list.txt")
diff --git a/birdnet_analyzer/species/utils.py b/birdnet_analyzer/species/utils.py
index 3260e58e0..7270148e7 100644
--- a/birdnet_analyzer/species/utils.py
+++ b/birdnet_analyzer/species/utils.py
@@ -17,20 +17,19 @@ def get_species_list(
     lat: float, lon: float, week: int | None, threshold: float, lang: MODEL_LANGUAGES
 ) -> list[str]:
     """
-    Generates a species list for a given location and time, and saves it to the
-    specified output path.
+    Generates a species list for a given location and time.
     Args:
         lat (float): Latitude of the location.
         lon (float): Longitude of the location.
-        week (int | None): Week of the year (1-52) for which the species list is
+        week (int | None): Week of the year (1-48) for which the species list is
                            generated. If None, all weeks are considered.
         threshold (float): Threshold for location filtering.
         lang (MODEL_LANGUAGES): Language code for species names.
     Returns:
-        list[str]: Species list as numpy strings.
+        list[str]: The species labels.
     """
     result = model_utils.run_geomodel(
         lat, lon, week, threshold=threshold, language=lang
     )
 
-    return [str(species) for species, prob in result.to_structured_array()]
+    return [str(species) for species, _ in result.to_structured_array()]
diff --git a/birdnet_analyzer/train/core.py b/birdnet_analyzer/train/core.py
index 79e3c3e63..5b1ccbe10 100644
--- a/birdnet_analyzer/train/core.py
+++ b/birdnet_analyzer/train/core.py
@@ -66,7 +66,8 @@ def train(
                                 Defaults to "checkpoints/custom/Custom_Classifier".
         crop_mode (Literal["center", "first", "segments", "smart"], optional): Mode for
             cropping audio samples. Defaults to "center".
-        overlap (float, optional): Overlap ratio for audio segments. Defaults to 0.0.
+        overlap (float, optional): Overlap of the segments in seconds for the
+            "segments" and "smart" crop modes. Defaults to 0.0.
         epochs (int, optional): Number of training epochs. Defaults to 50.
         batch_size (int, optional): Batch size for training. Defaults to 32.
         val_split (float, optional): Fraction of data to use for validation.
@@ -90,7 +91,8 @@ def train(
         upsampling_mode (Literal["repeat", "linear", "mean", "smote"], optional):
             Mode for upsampling. Defaults to "repeat".
         model_formats (list[Literal["tflite", "raven", "detached"]] | str, optional):
-            One or more formats to save the trained model. Defaults to "tflite".
+            One or more formats to save the trained model; "detached" is the
+            classifier alone, taking embeddings. Defaults to "tflite".
         model_save_mode (Literal["replace", "append"], optional): Save mode for the
             model. Defaults to "replace".
         save_cache_to (str | None, optional): Path to save the cache file.
@@ -112,8 +114,6 @@ def train(
         autotune_metric (Literal["val_AUPRC", "val_AUROC"], optional): Metric to
             optimize during hyperparameter tuning. Training also stops early on it and
             keeps the weights of its best epoch. Defaults to "val_AUPRC".
-        save_detached_classifier (bool, optional): Whether to additionally save a
-            detached version of the trained classifier. Defaults to False.
     Returns:
         None
     """
diff --git a/birdnet_analyzer/train/utils.py b/birdnet_analyzer/train/utils.py
index c24f8a4ef..c4ab649ab 100644
--- a/birdnet_analyzer/train/utils.py
+++ b/birdnet_analyzer/train/utils.py
@@ -636,18 +636,61 @@ def train_model(
     """Trains a custom classifier.
 
     Args:
+        audio_input: Training data folder, whose subfolder names are the labels, or a
+            cache file.
+        output: Path of the trained classifier. Defaults to
+            "checkpoints/custom/Custom_Classifier".
+        test_data: Test data folder, only used to evaluate the trained classifier.
+            Defaults to None.
+        crop_mode: "center", "first", "segments" or "smart". Defaults to "center".
+        overlap: Overlap of the segments in seconds for the "segments" and "smart"
+            crop modes. Defaults to 0.0.
+        epochs: Maximum number of epochs. Defaults to 50.
+        batch_size: Training batch size. Defaults to 32.
+        val_split: Fraction of the training data used for validation, above 0.
+            Defaults to 0.2.
+        learning_rate: Learning rate. Defaults to 0.0001.
+        weight_decay: AdamW weight decay. Defaults to 0.004.
+        use_focal_loss: Whether to use focal loss. Defaults to False.
+        focal_loss_gamma: Focal loss gamma. Defaults to 2.0.
+        focal_loss_alpha: Focal loss alpha. Defaults to 0.25.
+        hidden_units: Hidden units; above 0 the classifier has two layers.
+            Defaults to 0.
+        dropout: Dropout rate. Defaults to 0.0.
+        label_smoothing: Whether to use label smoothing. Defaults to False.
+        mixup: Whether to use mixup. Defaults to False.
+        upsampling_ratio: Ratio for upsampling minority classes. Defaults to 0.0.
+        upsampling_mode: "repeat", "linear", "mean" or "smote". Defaults to "repeat".
+        model_formats: One or more of "tflite", "raven" and "detached" (the
+            classifier alone, taking embeddings). Defaults to "tflite".
+        model_save_mode: "replace" the base model's classes or "append" to them.
+            Defaults to "replace".
+        save_cache_to: Path of a cache file to save the training data to.
+            Defaults to None.
+        threads: Number of threads decoding the training audio. Defaults to 1.
+        fmin: Lower bandpass frequency in Hz. Defaults to 0.0.
+        fmax: Upper bandpass frequency in Hz. Defaults to 15000.0.
+        audio_speed: Speed factor applied to the audio. Defaults to 1.0.
+        autotune: Whether to tune the hyperparameters first. Defaults to False.
+        autotune_trials: Number of tuning trials. Defaults to 50.
+        autotune_n_splits: Cross-validation folds per trial; 1 uses the validation
+            split. Defaults to 1.
+        autotune_n_repeats: Repetitions of the folds per trial. Defaults to 1.
+        autotune_metric: "val_AUPRC" or "val_AUROC", optimized by the tuning and the
+            early stopping. Defaults to "val_AUPRC".
         birdnet: The BirdNET version to train on. Its embeddings are the classifier's
             input, so the classifier only works with that version. A cache file keeps
-            the version it was embedded with.
-        on_epoch_end: A callback function that takes two arguments `epoch`, `logs`.
-        on_trial_result: A callback function for hyperparameter tuning.
-        on_data_load_end: A callback function for data loading progress.
-        autotune_directory: Directory for autotune results.
-        save_detached_classifier: Whether to additionally save a detached version of
-            the trained classifier.
+            the version it was embedded with. Defaults to the newest version.
+        on_epoch_end: Called with ``(epoch, logs)`` after each epoch of the final
+            training. Defaults to None.
+        on_trial_result: Called with the number of finished tuning trials, 0 before
+            the first. Defaults to None.
+        on_data_load_end: Called with ``(files_done, files_total, folder)`` for each
+            loaded file of a training or test data folder. Defaults to None.
 
     Returns:
-        A keras `History` object, whose `history` property contains all the metrics.
+        A tuple ``(history, metrics)``: the keras ``History`` of the final training,
+        and a dict of metrics on ``test_data``, or None without test data.
     """
     if autotune_metric not in get_args(AUTOTUNE_METRICS):
         raise ValueError(
diff --git a/docs/breaking-changes.rst b/docs/breaking-changes.rst
index fba11adbc..2661a2233 100644
--- a/docs/breaking-changes.rst
+++ b/docs/breaking-changes.rst
@@ -15,21 +15,22 @@ release (**v2.4.0**) and what to do about it.
 Default model is now BirdNET 3.0
 --------------------------------
 
-**What changed.** Analyses use the BirdNET 3.0 acoustic model, and location-based
-species lists use the 3.0 geolocation model, by default. v2.4 was the previous default.
+**What changed.** Analyses use the BirdNET 3.0 acoustic model by default, and
+location-based species lists always use the 3.0 geolocation model. v2.4 was the
+previous default for both.
 
 **Consequence.** Results are not directly comparable to v2.4 output: 3.0 is a different
 model with different confidence scores, a larger species set (11,560 labels) and an
 updated taxonomy. Do not mix or compare 3.0 result files with v2.4 ones.
 
-**What to do.** To keep the previous behaviour, select the 2.4 model explicitly:
+**What to do.** To keep the previous acoustic model, select the 2.4 model explicitly:
 
 .. code-block:: bash
 
    birdnet-analyze INPUT --birdnet 2.4
 
 In the GUI, choose *BirdNET 2.4* under model selection. The 2.4 model remains fully
-supported.
+supported. Location-based species lists come from the 3.0 geolocation model either way.
 
 
 Inference runs on ONNX, not TensorFlow, on the default path
@@ -68,11 +69,11 @@ automatically.
 **Consequence.**
 
 * Species that still cannot be matched (genuinely absent from the model, or a typo)
-  are **skipped with a warning**, and the analysis proceeds with the rest.
-* If **none** of the list's species can be matched, the analysis **errors** instead of
-  silently analysing every species.
-* Previously, an unknown entry in a ``--slist`` file aborted the run with a library
-  error; most such lists now succeed after reconciliation.
+  are **skipped with a warning**; v2.4.0 skipped them silently.
+* If **none** of the list's species can be matched, the analysis **errors**; v2.4.0
+  produced no detections.
+* ``--slist`` together with ``--lat`` and ``--lon`` is an error; v2.4.0 ignored the
+  list.
 
 **What to do.** Legacy lists generally work unchanged. To require that every entry
 match — turning any unmatched species into a hard error — use the new ``--strict``
@@ -86,26 +87,27 @@ In the GUI, selecting a species-list file shows a warning if it contains species
 chosen model does not have (only once that model has been downloaded).
 
 
-Label languages differ between 2.4 and 3.0
-------------------------------------------
+Label languages
+---------------
 
-**What changed.** The set of label languages (``--locale``) is not identical across
-model versions. Nine languages available in 2.4 are **not** in 3.0:
+**What changed.** The ``--locale`` codes depend on the model version. Compared with
+v2.4.0:
 
-   ``af``, ``ar``, ``en_uk``, ``hu``, ``it``, ``ko``, ``ro``, ``sl``, ``th``
+* ``en``, ``el``, ``he``, ``in``, ``is``, ``ml`` and ``pt_BR`` are no longer accepted.
+  English is ``en_us``, the new default.
+* The 3.0 model has no ``af``, ``ar``, ``en_uk``, ``hu``, ``it``, ``ko``, ``ro``,
+  ``sl`` or ``th``.
+* ``bg``, ``ca``, ``hr``, ``lt``, ``pt_pt`` and ``sr`` are only available with the 3.0
+  model, no longer with 2.4.
+* New with 3.0: ``cy``, ``es_ec``, ``es_es``, ``es_mx`` and ``fa``.
 
-Eleven languages are **new** in 3.0:
+**Consequence.** The command line rejects a code it does not know. A known code the
+selected model version lacks falls back to ``en_us``, with a warning. For example,
+``--locale it`` with the default 3.0 model produces English labels.
 
-   ``bg``, ``ca``, ``cy``, ``es_ec``, ``es_es``, ``es_mx``, ``fa``, ``hr``, ``lt``,
-   ``pt_pt``, ``sr``
-
-**Consequence.** Requesting a locale the selected model version does not support falls
-back to ``en_us``, with a warning. For example, ``--locale it`` with the default 3.0
-model produces English labels.
-
-**What to do.** Use a locale the model version supports, or switch to the 2.4 model if
-you need one of its dropped languages. In the GUI the locale dropdown offers only the
-selected model's languages.
+**What to do.** Use a locale the model version supports, or switch model versions for
+one only the other has. In the GUI the locale dropdown offers only the selected model's
+languages.
 
 
 Training defaults to the 3.0 base
@@ -131,22 +133,200 @@ explicit ``-o/--output`` to pin a location. The new default is writable on a
 pip-installed package and survives upgrades.
 
 
-Resuming an interrupted analysis re-checks changed files
---------------------------------------------------------
+Training validates on the training data
+---------------------------------------
+
+**What changed.** ``--test_data`` now only evaluates the trained classifier. A
+validation split (``--val_split``, which must be above 0) is always taken from the
+training data, and early stopping watches ``--autotune_metric`` (AUPRC by default)
+instead of the validation loss. The AUPRC and AUROC reported during training no longer
+apply the sigmoid twice, and ``--label_smoothing`` uses the standard formula.
+
+**Consequence.** With ``--test_data``, the classifier trains on the training data minus
+the validation split; v2.4.0 trained on all of it and validated on the test data. The
+same command trains a different classifier than v2.4.0, and its validation scores are
+not comparable.
+
+**What to do.** Choose ``--val_split`` for the share of the training data to hold out;
+``--val_split 0`` is rejected. Compare scores only between runs of this version.
+
+
+Locations and species lists
+---------------------------
+
+**What changed.**
+
+* ``-1`` no longer means "not set". ``--lat -1 --lon -1`` is a location in the Gulf of
+  Guinea, and ``--week -1`` with a location is an error. Leave an option out instead.
+* ``analyze`` and ``species`` reject only one of ``--lat`` and ``--lon``; v2.4.0 used
+  -1 for the missing one.
+* ``birdnet_analyzer.species`` without a location writes every species of the default
+  acoustic model (11,560 for BirdNET 3.0), in the model's order. v2.4.0 wrote the few
+  species the geo model predicted for latitude and longitude -1.
+* Lists for a location come from the 3.0 geolocation model and include species the
+  acoustic model does not have, such as insects. They are sorted alphabetically; v2.4.0
+  used the acoustic model's labels and sorted them by occurrence frequency.
+
+**Consequence.** Scripts that pass -1 get a filter for the wrong place or an error.
+Using a location list as ``--slist`` logs the species the model lacks as skipped, and
+fails with ``--strict``. Lists without a location are much longer, and the first such
+run downloads the acoustic model.
+
+**What to do.** Omit ``--lat``, ``--lon`` and ``--week`` rather than passing -1, and
+drop ``--strict`` for lists made by ``birdnet_analyzer.species``.
+
+
+Top-N results keep the confidence threshold
+-------------------------------------------
+
+**What changed.** ``--top_n`` keeps at most N detections per segment among those above
+``--min_conf``. v2.4.0 ignored the threshold when ``--top_n`` was set.
+
+**Consequence.** The same command returns fewer detections than in v2.4.0.
+
+**What to do.** Pass a low threshold, such as ``--min_conf 0.00001``, to get the top N
+regardless of their score.
+
+
+Results are written to combined files by default
+------------------------------------------------
+
+**What changed.** An analysis writes one combined result file per result type, such as
+``BirdNET_SelectionTable.txt``. v2.4.0 wrote one file per audio file and added
+combined files only with ``--combine_results``.
+
+**Consequence.** Scripts that expect a ``<audio name>.BirdNET.selection.table.txt``
+(or another per-file result) next to each recording find nothing.
+
+**What to do.** Pass ``--split_tables`` for one result file per audio file, which
+replaces the combined files. Unlike v2.4.0, it writes no file for an audio file without
+detections.
+
+
+Result and parameters file formats
+----------------------------------
+
+**What changed.**
+
+* Raven selection tables add a ``Scientific Name`` column and order the columns
+  differently. They have no ``nocall`` row for a file without detections, and no
+  ``BirdNET_SelectionTable.list.txt`` is written.
+* Audacity labels read ``Scientific name_Common name`` instead of
+  ``Scientific name, Common name``.
+* The analysis parameters are written to ``birdnet.analyze-params.csv`` instead of
+  ``BirdNET_analysis_params.csv``. ``analyze()`` in Python writes it only with
+  ``save_params=True``; v2.4.0 always wrote it.
+* The parameters of a trained classifier go to ``<classifier>.birdnet.train-params.csv``
+  instead of ``<classifier>_Params.csv``.
+
+**Consequence.** Scripts that read these files by column position, label format or
+file name break.
+
+**What to do.** Read the columns by name. ``--load_params`` reads both the new and the
+v2.4.0 parameters files.
+
+
+Kaleidoscope output has seven columns
+-------------------------------------
+
+**What changed.** ``--rtype kaleidoscope`` writes ``INDIR``, ``FOLDER``, ``IN FILE``,
+``OFFSET``, ``DURATION``, ``TOP1MATCH`` and ``TOP1DIST``. v2.4.0 wrote 13 columns: the
+scientific and common name, ``confidence``, ``lat``, ``lon``, ``week``, ``overlap`` and
+``sensitivity``.
+
+**Consequence.** Scripts that read the old columns break. ``TOP1MATCH`` holds the
+common name and ``TOP1DIST`` the confidence, where higher is better, unlike
+Kaleidoscope's own distance. ``birdnet_analyzer.segments`` still reads v2.4.0
+Kaleidoscope files.
+
+**What to do.** For the scientific name or the analysis settings, use ``--rtype csv``
+with ``--additional_columns``.
+
+
+Custom classifiers must be tflite files
+---------------------------------------
+
+**What changed.** ``--classifier`` takes a ``.tflite`` file only; v2.4.0 also loaded a
+Raven model folder. The labels are read from ``<classifier>_Labels.txt`` or
+``--cc_species_list``; v2.4.0 also found ``<name>_Labels.txt`` next to an older
+BirdNET ``<name>_Model_FP32.tflite``.
+
+**Consequence.** A Raven model folder fails to load, and an older BirdNET model needs
+its labels file named explicitly.
+
+**What to do.** Use the tflite export of a classifier, and pass ``--cc_species_list``
+for older BirdNET models.
+
+
+Search needs the audio root and a new database
+----------------------------------------------
+
+**What changed.** ``birdnet_analyzer.search`` requires ``--audio_root``, the folder the
+audio paths in the database are relative to, and ``search()`` takes it as its third
+argument, before ``queryfile``. Embedding databases now use the perch-hoplite 1.x
+format; v2.4.0 wrote them with perch-hoplite 0.x.
+
+**Consequence.** v2.4.0 search command lines and positional calls of ``search()`` fail.
+Databases written by v2.4.0 are in the older format.
+
+**What to do.** Pass ``--audio_root`` (the parent of the input that was embedded), and
+extract the embeddings again into a new database.
+
+
+Evaluation needs a confidence column and scores every annotated recording
+-------------------------------------------------------------------------
+
+**What changed.**
+
+* A prediction table without the mapped confidence column, or with an empty one, is an
+  error; v2.4.0 scored its predictions as confidence 0.
+* Without ``--recording_duration``, a recording that has annotations but no predictions
+  is scored, its length taken from the annotations; v2.4.0 dropped it.
+* A recording that has predictions but no annotation file is dropped; v2.4.0 counted
+  its predictions as false positives.
+* The default ``--metrics`` are ``auroc precision recall f1 ap`` instead of
+  ``accuracy precision recall``.
+
+**Consequence.** An evaluation with a wrong column mapping fails instead of reporting
+meaningless metrics, and the same evaluation reports other metrics and other values
+than in v2.4.0.
+
+**What to do.** Map the confidence column with ``--columns_predictions``. Pass
+``--recording_duration`` if every recording has the same length,
+``--score_unannotated_as_empty`` to count unannotated recordings, and ``--metrics`` for
+the v2.4.0 metrics.
+
+
+Removed options and parameters
+------------------------------
+
+**What changed.** These v2.4.0 command-line options no longer exist, so command lines
+that use them fail with "unrecognized arguments". The Python functions lost the
+matching parameters.
+
+* ``analyze``: ``--threads`` (use ``--n_workers``), ``--combine_results`` (see above)
+  and ``--skip_existing_results`` (an interrupted directory analysis resumes instead; a
+  finished one runs again). ``analyze()`` also lost ``use_perch``; pass
+  ``model="perch"``.
+* ``embeddings``: ``--threads`` (use ``--n_workers``).
+* ``species``: ``--sortby``.
+* ``train``: ``--model_format`` (use ``--model_formats``, ``tflite raven`` for
+  ``both``), ``--cache_mode`` and ``--cache_file`` (pass a cache file as ``INPUT`` to
+  train from it, and ``--save_cache_to`` to write one), and
+  ``--autotune_executions_per_trial`` (``--autotune_n_splits`` with
+  ``--autotune_n_repeats`` repeats each trial).
 
-**What changed.** A directory analysis now keeps a crash-safe journal so an interrupted
-run can be continued: re-running the same analysis skips files that already completed.
-Each file's stored result is keyed on its path **and its size and modification time**.
+The HTTP server and client, ``birdnet_analyzer.network.server`` and ``.client``, were
+removed.
 
-**Consequence.** When a run is resumed, an input file that changed on disk (edited,
-regenerated, or merely touched so its modification time moved) since it was first
-analysed is **re-analysed** rather than reusing the earlier result. Unchanged files are
-skipped as before.
+**What to do.** Update scripts as listed.
 
 
 Other changes
 -------------
 
-* **Python 3.11+** is required (tested on 3.11–3.13).
 * **Perch** is not available on macOS — it needs a TensorFlow build that is not yet
   published for that platform.
+* **Search** with a query longer than one segment (``--crop_mode segments``) ranks the
+  results by their mean score over all query segments. v2.4.0 counted a result only for
+  the query segments whose own top results contained it, so its rankings differ.
diff --git a/docs/models.rst b/docs/models.rst
index f68865fa0..c036409e5 100644
--- a/docs/models.rst
+++ b/docs/models.rst
@@ -43,7 +43,7 @@ Older models can also be used as custom classifiers in the GUI or using the `--c
 Just download your desired model version and unzip.
 
 * GUI: Select the \*_Model_FP32.tflite file under **Species selection / Custom classifier**
-* CLI: ``python -m birdnet_analyzer ... --classifier 'path_to_Model_FP32.tflite'``
+* CLI: ``python -m birdnet_analyzer.analyze ... --classifier 'path_to_Model_FP32.tflite' --cc_species_list 'path_to_Labels.txt'``
 
 Model Version History
 ---------------------
diff --git a/docs/usage/cli.rst b/docs/usage/cli.rst
index a4b6c41bb..2ef0d745d 100644
--- a/docs/usage/cli.rst
+++ b/docs/usage/cli.rst
@@ -21,7 +21,7 @@ birdnet_analyzer.analyze
 
    .. code:: bash
 
-      python3 -m birdnet_analyzer.analyze example/ --slist example/ --min_conf 0.5 --threads 4
+      python3 -m birdnet_analyzer.analyze example/ --slist example/ --min_conf 0.5 --n_workers 4
 
       python3 -m birdnet_analyzer.analyze example/ --lat 42.5 --lon -76.45 --week 4 --sensitivity 1.0
 
@@ -84,7 +84,7 @@ birdnet_analyzer.train
    All you need is a dataset of labeled audio files, organized in folders by species (we use folder names as labels).
    This also works for non-bird species, as long as you have a dataset of labeled audio files.
    
-   Audio files will be resampled to the sample rate of the base model (48 kHz for BirdNET 2.4, 32 kHz for BirdNET 3.0) and converted into 3-second segments (we support different crop segmentation modes for files longer than 3 seconds; we pad with random noise if the file is shorter). We recommend using at least 100 audio files per species (although training also works with less data).
+   Audio files will be resampled to the sample rate of the base model (48 kHz for BirdNET 2.4, 32 kHz for BirdNET 3.0) and converted into 3-second segments (we support different crop segmentation modes for files longer than 3 seconds; we pad with zeros if the file is shorter). We recommend using at least 100 audio files per species (although training also works with less data).
 
    The classifier is trained on the embeddings of one BirdNET version, chosen with ``--birdnet`` (3.0 by default), and only works with that version.
    
@@ -112,7 +112,7 @@ birdnet_analyzer.train
       python -m birdnet_analyzer.analyze example/ --classifier checkpoints/custom/Custom_Classifier.tflite
 
    .. note::
-      Setting a custom classifier will also set the new labels file. Due to these custom labels, the location filter and locale will be disabled.
+      Setting a custom classifier will also set the new labels file. The species names come from that file, so ``--locale`` has no effect. A location (``--lat``/``--lon``) keeps only the classifier's species that the geo model predicts there, or, with a warning, all of them if it predicts none.
    
    **Negative samples**
 
diff --git a/tests/analyze/test_analyze.py b/tests/analyze/test_analyze.py
index 56170e366..da6ca5cb9 100644
--- a/tests/analyze/test_analyze.py
+++ b/tests/analyze/test_analyze.py
@@ -543,6 +543,64 @@ def test_analyze_without_location_passes_no_species_list(
     assert call_kwargs["custom_species_list"] is None
 
 
+@patch("birdnet_analyzer.model_utils.run_inference")
+def test_analyze_takes_no_show_progress_argument(
+    mock_run_inference, setup_test_environment
+):
+    env = setup_test_environment
+    mock_run_inference.return_value = object()
+
+    with pytest.raises(TypeError, match="show_progress"):
+        analyze(
+            env["input_dir"], env["output_dir"], show_progress=True, _return_only=True
+        )
+
+
+@pytest.mark.parametrize("coordinate", [{"lat": 50.83}, {"lon": 12.92}])
+@patch("birdnet_analyzer.model_utils.run_geomodel")
+@patch("birdnet_analyzer.model_utils.run_inference")
+def test_analyze_rejects_a_single_coordinate(
+    mock_run_inference, mock_run_geomodel, coordinate, setup_test_environment
+):
+    env = setup_test_environment
+
+    with pytest.raises(ValueError, match="both lat and lon"):
+        analyze(env["input_dir"], env["output_dir"], _return_only=True, **coordinate)
+
+    mock_run_geomodel.assert_not_called()
+    mock_run_inference.assert_not_called()
+
+
+@patch("birdnet_analyzer.model_utils.run_inference")
+def test_analyze_rejects_perch_with_a_custom_classifier(
+    mock_run_inference, setup_test_environment
+):
+    env = setup_test_environment
+
+    with pytest.raises(ValueError, match="Perch"):
+        analyze(
+            env["input_dir"],
+            env["output_dir"],
+            model="perch",
+            classifier="classifier.tflite",
+            _return_only=True,
+        )
+
+    mock_run_inference.assert_not_called()
+
+
+@patch("birdnet_analyzer.model_utils.run_inference")
+def test_analyze_runs_perch_without_a_custom_classifier(
+    mock_run_inference, setup_test_environment
+):
+    env = setup_test_environment
+    mock_run_inference.return_value = object()
+
+    analyze(env["input_dir"], env["output_dir"], model="perch", _return_only=True)
+
+    assert mock_run_inference.call_args.kwargs["model"] == "perch"
+
+
 def test_match_species_to_model_joins_on_scientific_name():
     """Requested species map onto a model's labels by scientific name; unknowns are
     reported as unmatched."""
@@ -656,19 +714,28 @@ def test_reconcile_species_list_strict_raises(tmp_path):
         )
 
 
-def test_reconcile_species_list_all_unmatched_raises_even_without_strict(tmp_path):
+def test_reconcile_species_list_all_unmatched_raises_even_without_strict(
+    tmp_path, caplog
+):
     """A user list where nothing reconciles must error, not return an empty set: the
     library reads an empty custom list as 'no filter' and would analyze every species,
     the opposite of the user's intent - so this is an error even in the default mode."""
+    import logging
+
     from birdnet_analyzer.model_utils import _reconcile_species_list
 
     slist = tmp_path / "species_list.txt"
     slist.write_text("Foo bar_Not A Bird\nBaz qux_Also Not\n", encoding="utf-8")
-    with pytest.raises(ValueError, match="None of the 2 species"):
+    with (
+        caplog.at_level(logging.WARNING),
+        pytest.raises(ValueError, match="None of the 2 species"),
+    ):
         _reconcile_species_list(
             str(slist), ["Turdus migratorius_American Robin"], strict=False
         )
 
+    assert "not filtered" not in caplog.text
+
 
 # The BirdNET example species list as it was before the 3.0 taxonomy update - a
 # realistic "legacy" list a user may still have on disk. Five labels were revised in
@@ -783,6 +850,23 @@ def test_reconcile_species_list_geo_collection_is_silent(caplog):
     assert caplog.text == ""
 
 
+@pytest.mark.parametrize("requested", [{"Pekania pennanti_Fisher"}, set()])
+def test_reconcile_species_list_warns_when_a_collection_matches_nothing(
+    requested, caplog
+):
+    import logging
+
+    from birdnet_analyzer.model_utils import _reconcile_species_list
+
+    with caplog.at_level(logging.WARNING):
+        matched = _reconcile_species_list(
+            requested, ["Turdus migratorius_American Robin"], strict=False
+        )
+
+    assert matched == set()
+    assert "not filtered" in caplog.text
+
+
 def test_an_analysis_without_detections_writes_every_result_type(
     setup_test_environment,
 ):
@@ -913,6 +997,19 @@ def test_the_cli_warns_when_additional_columns_cannot_be_written(
     ] == expected_warnings
 
 
+def test_the_cli_writes_the_parameters_file(tmp_path, monkeypatch):
+    monkeypatch.setattr(settings, "ERROR_LOG_FILE", str(tmp_path / "error_log.txt"))
+    monkeypatch.setattr("sys.argv", ["birdnet-analyze", str(tmp_path)])
+
+    with patch.object(analyze_cli, "analyze") as mock_analyze:
+        try:
+            analyze_cli.main()
+        finally:
+            logs._remove_installed_handlers()
+
+    assert mock_analyze.call_args.kwargs.get("save_params") is True
+
+
 def _link_directory(link, target):
     if os.name == "nt":
         subprocess.run(
diff --git a/tests/species/test_species.py b/tests/species/test_species.py
index 4a247ffa8..b00444118 100644
--- a/tests/species/test_species.py
+++ b/tests/species/test_species.py
@@ -6,6 +6,7 @@
 import pytest
 
 from birdnet_analyzer.cli import species_parser
+from birdnet_analyzer.config import DEFAULT_ACOUSTIC_MODEL_VERSION
 from birdnet_analyzer.species.core import species
 
 
@@ -24,23 +25,53 @@ def setup_test_environment():
     shutil.rmtree(test_dir)
 
 
-@patch("birdnet_analyzer.species.utils.get_species_list")
-def test_species_cli(mock_get_species_list, setup_test_environment):
+@pytest.mark.parametrize(
+    ("locale_args", "locale"), [([], "en_us"), (["--locale", "de"], "de")]
+)
+@patch("birdnet_analyzer.model_utils.run_geomodel")
+@patch("birdnet_analyzer.model_utils.acoustic_species_list")
+def test_species_cli(
+    mock_acoustic_species_list,
+    mock_run_geomodel,
+    locale_args,
+    locale,
+    setup_test_environment,
+):
     env = setup_test_environment
 
-    mock_get_species_list.return_value = ["Species1", "Species2"]
+    mock_acoustic_species_list.return_value = ["Species1", "Species2"]
 
     parser = species_parser()
-    args = parser.parse_args([env["output_dir"]])
-    kwargs = vars(args)
-    kwargs.pop("sortby", None)
+    args = parser.parse_args([*locale_args, env["output_dir"]])
 
-    species(**kwargs)
+    species(**vars(args))
 
-    mock_get_species_list.assert_called_once_with(
-        lat=-1, lon=-1, week=None, threshold=0.03, lang="en_us"
+    mock_run_geomodel.assert_not_called()
+    mock_acoustic_species_list.assert_called_once_with(
+        DEFAULT_ACOUSTIC_MODEL_VERSION, locale
     )
 
+    with open(
+        os.path.join(env["output_dir"], "species_list.txt"), encoding="utf-8"
+    ) as f:
+        assert f.read().splitlines() == ["Species1", "Species2"]
+
+
+@pytest.mark.parametrize("coordinate", [{"lat": 50.83}, {"lon": 12.92}])
+@patch("birdnet_analyzer.model_utils.run_geomodel")
+@patch("birdnet_analyzer.model_utils.acoustic_species_list")
+def test_species_rejects_a_single_coordinate(
+    mock_acoustic_species_list, mock_run_geomodel, coordinate, setup_test_environment
+):
+    env = setup_test_environment
+
+    with pytest.raises(ValueError, match="both lat and lon"):
+        species(env["output_dir"], **coordinate)
+
+    mock_run_geomodel.assert_not_called()
+    mock_acoustic_species_list.assert_not_called()
+    assert not os.listdir(env["output_dir"])
+
 
 @patch("birdnet_analyzer.species.utils.get_species_list")
 def test_species_cli_accepts_full_parser_surface(
diff --git a/tests/test_cli.py b/tests/test_cli.py
index 4ebdce5a4..c1d948564 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -1,6 +1,9 @@
+from typing import get_args
+
 import pytest
 
 from birdnet_analyzer import cli
+from birdnet_analyzer import config as cfg
 
 
 @pytest.mark.parametrize(
@@ -18,3 +21,22 @@ def test_the_overlap_help_states_the_cap_the_parser_applies(parser, cap):
     assert overlap.type("100") == cap
     assert overlap.type("-1") == 0.0
     assert f"[0.0, {cap}]" in overlap.help
+
+
+@pytest.mark.parametrize(
+    ("parser", "dest", "values"),
+    [
+        (cli.analyzer_parser, "rtype", cfg.RESULT_TYPES),
+        (cli.search_parser, "score_function", cfg.SCORE_FUNCTIONS),
+        (cli.search_parser, "crop_mode", cfg.CROP_MODES),
+        (cli.train_parser, "crop_mode", cfg.SAMPLE_CROP_MODES),
+        (cli.train_parser, "upsampling_mode", cfg.UPSAMPLING_MODES),
+        (cli.train_parser, "model_formats", cfg.TRAINED_MODEL_OUTPUT_FORMATS),
+        (cli.train_parser, "model_save_mode", cfg.TRAINED_MODEL_SAVE_MODES),
+        (cli.train_parser, "autotune_metric", cfg.AUTOTUNE_METRICS),
+    ],
+)
+def test_the_cli_choices_are_the_values_of_their_config_type(parser, dest, values):
+    action = next(action for action in parser()._actions if action.dest == dest)
+
+    assert set(action.choices) == set(get_args(values))
diff --git a/tests/train/test_train.py b/tests/train/test_train.py
index fe5471ed1..a54818a8b 100644
--- a/tests/train/test_train.py
+++ b/tests/train/test_train.py
@@ -2,14 +2,12 @@
 import os
 import shutil
 import tempfile
-from typing import get_args
 from unittest.mock import MagicMock, patch
 
 import numpy as np
 import pytest
 
 from birdnet_analyzer.cli import train_parser
-from birdnet_analyzer.config import UPSAMPLING_MODES
 from birdnet_analyzer.train.core import train
 
 
@@ -258,12 +256,6 @@ def test_the_cli_writes_no_training_cache_unless_asked(mock_train_model, tmp_pat
     assert mock_train_model.call_args[1]["save_cache_to"] is None
 
 
-def test_the_upsampling_modes_type_lists_every_mode_the_cli_accepts():
-    action = next(a for a in train_parser()._actions if a.dest == "upsampling_mode")
-
-    assert set(get_args(UPSAMPLING_MODES)) == set(action.choices)
-
-
 def test_the_api_and_the_cli_default_to_the_same_number_of_autotune_folds():
     api_default = inspect.signature(train).parameters["autotune_n_splits"].default