Comparison of autonomous and manual recording methods for discrimination of individually distinctive Ovenbird songs
Bibliographic record
Abstract
Many animals produce individually distinctive vocalizations with increased outputs during the breeding season. Many animals, including birds, can recognize other individuals based on the distinctive features of their songs and researchers can use bioacoustics tools to discriminate among individuals. Typically, bioacoustics analyses use recordings made with highly directional microphones that are free of background noise and spectral overlap. However, recent technological advances in automated recording have made it possible to record remotely and cover larger areas simultaneously. We tested whether spectrogram cross-correlation can be used to discriminate among songs of 19 individual Ovenbirds (Seiurus aurocapillus). We used two microphone types: directional (Sennheiser MKH-70) and omnidirectional (SMX-II) microphones. Because birds may vary in their distance from the SMX-II microphones, songs were selected as either high-quality (close to the recorder) or low-quality (further away from the recorder). We found that all recording types could be used to discriminate the songs of individual male Ovenbirds from other males in the population. Discrimination among directional recordings was significantly better than among omnidirectional recordings, and high-quality recordings could be used to discriminate among individuals significantly better than low-quality recordings. Taken together, our results suggest that automated omnidirectional recording could be valuable for future behavioural research allowing individuals to be followed over an entire breeding season. In addition, acoustic surveys of communities could provide information about abundance as well as presence and/or absence of species.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".