Using vocal individuality to monitor Queen Charlotte Saw-whet owls (Aegolius acadicus brooksi)
Bibliographic record
Abstract
Our goal was to assess whether male Northern Saw-whet Owls (Aegolius acadicus brooksi) in the Queen Charlotte Islands have sufficient variation in the notes of their territorial/advertisement call to identify individuals within and between breeding seasons.Recordings of calling bouts were collected from 24 sites over the 2-yr study period and analyzed using discriminant function analysis.Using recordings of eight sites in 2002 and 16 sites in 2003, we correctly identified novel notes collected during a single recording session for 69% and 75% of the samples, respectively.Of the 13 sites for which we had multiple nights of recordings, 73% of notes measured from a novel night were classified to the correct site.The data also suggested that vocal individuality can be used to monitor site fidelity and rates of territory turnover of male owls across years, as correct classification of males re-occupying specific sites across years (>60%) was greater than rates of random classification (i = 3.3%, maximum = 19%).Vocal individuality, thus, appears to have promise as a useful tool for monitoring and studying life history parameters of Northern Saw-whet Owls both within and between years.KEY WOP, DS: Northern Saw-whet Owl;
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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 | 0.000 | 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) | 0.001 | 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".