Wintering Snowy Owls<i>Bubo scandiacus</i>integrate plumage colour, behaviour and their environment to maximize efficacy of visual displays
Bibliographic record
Abstract
For a comprehensive understanding of the evolution of animal signals, it is necessary to understand how the performance of visual displays is maximized to get the most possible attention from receivers. We assessed whether the white plumage of Snowy OwlsBubo scandiacusfunctioned as a social signal and, if so, how coloration and behavioural adaptations enhance signal efficacy. Signalling theory predicts that: (1) the colour properties of plumage should vary across the body, with the brightest parts being those involved in visual display performance; (2) specific displays calling attention to or enhancing detection or conspicuousness to conspecifics should be evident; and (3) location of the signallers should be such that signal efficacy is optimized. All three predictions were supported. The brightest areas of the plumage (particularly the face, throat and upper breast) were always unspotted, and white is particularly effective in open habitats characteristic of this species. The birds displayed a specific posture and orientated toward the sun preferentially on sunny days, and Owls with the whitest (least spotted) plumage displayed more and signalled more frequently from perches on the ground, where albedo from the snow may enhance the visual display. Snowy Owls integrate coloration, behaviour and environment through habitat selection to maximize the efficacy of their visual displays.
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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".