Vocal individuality as a potential long-term monitoring tool for Western Screech-owls, <i>Megascops kennicottii</i>
Bibliographic record
Abstract
Recent studies suggest that individually distinctive vocalizations found in many avian species can be used in population monitoring. In this study we assessed whether vocal identification of male Western Screech-owls ( Megascops kennicottii (Elliot, 1867)) was possible, and if it could be applied as a long-term monitoring tool. Recordings were collected between 2001 and 2003 from 28 territories on southern and central Vancouver Island. As a quantitative descriptor of the calls, a total of 17 variables were measured from each of 1125 calls. A discriminant function analysis resulted in 92.3% of calls being correctly classified to individual territories within one season and 87.3% of calls in a cross-validation of the model. Variables that showed the greatest discriminant ability included length of call, internote distance between first note and second note, and number of notes per call. Of the 14 territories that had owl calls recorded over 2 years, 4 appeared to be occupied by a different individual in the 2nd year, 7 had calls that were consistent between years, and 3 had calls that were ambiguously classified between years. Our results suggest that Western Screech-owl calls have enough individually recognizable characteristics to aid in the tracking of individuals both within and between years, allowing for long-term monitoring of individuals.
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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 | 0.001 |
| 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".