BatScope manages acoustic recordings, analyses calls, and classifies bat species automatically
Bibliographic record
Abstract
BatScope is a free application for processing acoustic high-frequency recordings of bats. It can import data, including meta-data information, from recorders such as Batlogger. The resulting content can be filtered visually as spectrograms or according to data fields and can be displayed. Automated processing includes detecting and extracting of echolocation calls, filtering noise, and measuring statistical parameters. Calls are classified to species by statistically matching to a reference database. A weighted list of classifiers helps to assign the most likely species per call. Classifiers were trained on 19 636 echolocation calls of 27 European bat species. When classifiers all agree on a species (76.4% of all cases), the mean correct classification rate reaches 95.7%. A sequence’s summary statistic indicates the most likely species occurring therein. Classifications can be verified visually, by filtering, and by acoustic comparison with reference calls. Procedures are available for, e.g., excluding dubious cutouts from the statistics and for accepting or overriding the proposed species assignment. Acoustic recordings can be exported and exchanged with other users. Finally, the verified results can be exported to spreadsheets for further analyses and reporting. We currently reprogram BatScope using Java, PostgreSQL, and R to reach a unified and portable software architecture.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.032 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".