Uncovering Breeding Habitat Use of an Uncommon Songbird in Pennsylvania Using Large Scale Acoustic Data
Bibliographic record
Abstract
ABSTRACT The Canada Warbler (Cardellina canadensis) population has declined by over 60% across its range since 1966. Knowledge of breeding habitat use is crucial to the conservation of songbird species but is missing for significant portions of the breeding range of the Canada Warbler's genetically distinct eastern population. We used a large‐scale deployment of autonomous recording units (ARUs) to record Canada Warbler song across north‐central and southwestern Pennsylvania. We deployed ARUs at 664 locations in forested ecosystems, setting them to record for 2 h each morning from mid‐May to mid‐June. We used classifier‐assisted listening to detect Canada Warbler song and generate detection histories. Our occupancy models revealed six significant habitat associations. We highlight forest conditions that can be targeted for the conservation of steeply declining eastern Canada Warbler populations. Our study is an example of the gains in statistical power allowed by ARUs and machine learning methods.
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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 | 0.001 |
| 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 | 0.000 |
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".