Utility of Automated Species Recognition For Acoustic Monitoring of Owls
Bibliographic record
Abstract
Presence or abundance of owls is frequently assessed using call-broadcast surveys to elicit responses and increase detection rates, but can draw owls in from a distance and could affect conclusions about fine-scale habitat associations. Passive acoustic surveys with field personnel or autonomous recording units (ARUs) may be a less biased method for surveying owls. Automated recognition techniques have proven useful to process large volumes of acoustic recordings from ARUs, and we sought to test the utility of automated recognition for three owl species. We built templates or “recognizers” for the territorial calls of the Barred Owl (Strix varia), the Boreal Owl (Aegolius funereus), and the Great Horned Owl (Bubo virginianus). We assessed the performance of each recognizer by evaluating precision, processing time, and false negatives. We used ARUs to survey for owls in northeastern Alberta, Canada, and compared the results from the recognizers to results from researchers listening to a subsample of the recordings. We verified the results to filter out false positives, but verification time was substantially lower than time spent listening. We processed more recordings and obtained a larger dataset of owl detections than would have been possible with either listening to the recordings only or conducting traditional field surveys without ARUs, suggesting a significant benefit of automated recognition. Precision was quite variable, but false negatives were relatively low and did not affect results of owl habitat associations. Given the relatively low detection rates of owls by listening to recordings, an automated recognition approach is likely to be highly useful for monitoring owls.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".