Male–male vocal interactions in a territorial neotropical quail: which song characteristics predict a territorial male's response?
Bibliographic record
Abstract
Abstract Males singing within their territories can change their song characteristics in order to interact with conspecifics; males may respond to territorial intrusions by vocalizing, approaching the intruder and/or displaying. I studied male–male interactions by quantifying vocal and behavioural responses of male spot-bellied bobwhites (Colinus leucopogon) toward playback of conspecific male songs. Male responses toward playback song depended on the quality of the territorial male's song relative to the playback stimulus. In this species males who sang songs with higher peak and low frequency, longer song duration, and lower song rate were less responsive to simulated territorial intrusions. Spot-bellied bobwhite males that sang in response to the playback increased the low frequencies of their songs relative to pre-playback song, a vocal behaviour related to dominance in males of other species. Males that approached the speaker sang longer songs, a characteristic associated with increased aggression or motivation to fight in other bird species. The results of this playback experiment suggest that male spot-bellied bobwhite song characteristics according to playback characteristics predict response to territorial intrusions and may, therefore, play an important role in male–male interactions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".