SINGING BEHAVIOR VARIES WITH BREEDING STATUS OF AMERICAN REDSTARTS (SETOPHAGA RUTICILLA)
Bibliographic record
Abstract
We examined the relationship between singing behavior and breeding status in the American Redstart (Setophaga ruticilla) by analyzing song rates, singing mode (Repeat or Serial), and variability of song delivery in relation to the age and breeding status of 129 males in the Hubbard Brook Experimental Forest, New Hampshire.Unpaired males spent most of their time (Ͼ90%) after dawn singing in Repeat mode, whereas paired males sang sporadically, in Serial as well as Repeat mode (51% of their singing time).Males who lost their mates sang in Repeat mode at rates indistinguishable from males who had not yet obtained a mate.Overall, unpaired males sang in Repeat mode at significantly higher and less variable rates than did paired males.Although a larger proportion of second-year males were unpaired than after-second-year males, we found no evidence that age affected singing behavior.We also assessed the effect of pairing status on male detectability in song-based monitoring surveys (e.g., point counts), and we suggest a field protocol for identifying unpaired males.Simulations of 5-min field samples, obtained from continuous samples Ͼ3 hr in duration, suggest that human listeners would be twice as likely to detect unpaired males as paired males.This result suggests that surveys based on aural detections may be biased in favor of unpaired males.In our population, Ͼ90% of males who sang Ͼ40 Repeat songs in 5 min were unpaired.Unpaired males were Ͼ3 times as likely as paired males to sing only Repeat songs in a given 5-min period.These results suggest that it may be possible to identify unpaired male American Redstarts by their high singing rates of exclusively Repeat songs.
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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 | 0.000 |
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".