Unsupervised discrimination of male Tawny owls ( <i>Strix aluco</i> ) individual calls using robust measurements of the acoustic signal
Bibliographic record
Abstract
Vocal individuality has been widely documented in the Tawny owl (Strix aluco); however, all statistical tools employed thus far to discriminate individual vocalisations have relied on prior knowledge regarding number and identity of individuals. In this study, we tested the effectiveness of four unsupervised clustering algorithms in distinguishing among eight Tawny owl males, solely based on acoustic characteristics of their vocalisations. We also employed both traditional bound-based and robust measurements of acoustic signal to compare their efficacy. We finally evaluated the applicability of this method in identifying the number and distribution of the remaining males recorded in our study area. Three of the four unsupervised techniques had a high rate of success in discriminating among vocalisations of the eight males. In all cases, the best results were obtained using robust measurements. However, when extending the analysis to the remaining unknown males recorded, the highest rate of misclassification errors made results more difficult to interpret. Our study provided a useful tool to discriminate male Tawny owls when only their call recordings are available. Furthermore, this method could be extended to other nocturnal and vociferous species, representing one of the few existing approaches for unsupervised classification of individuals based on acoustic features.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".