Flock formation and the role of plumage colouration in Ardeidae
Bibliographic record
Abstract
It has been hypothesized that white plumage facilitates flock formation in Ardeidae. We conducted four experiments using decoys to test factors involved in attracting wading birds to a specific pond. The first three experiments tested the effects of plumage colouration, flock size, and species-specific decoys on flock formation. The fourth experiment examined intraspecific differences in flock choice between the two colour morphs of the reddish egret, Egretta rufescens (Gmelin, 1789). Wading birds landed at flocks of decoys more often than single or no decoys (P < 0.001) but exhibited no overall attraction to white plumage (P > 0.05). White-plumaged species were attracted to white decoys (P < 0.001) and dark-plumaged species were attracted to dark decoys (P < 0.001). Snowy egrets (E. thula (Molina, 1782)), great egrets (Ardea alba L., 1758), and little blue herons (E. caerulea (L., 1758)) landed more often at ponds that contained decoys resembling conspecifics. At the intraspecific level, all observed reddish egrets selected flocks with like-plumaged decoys. Our results suggest that plumage colouration is an attractant for species with similar plumage, but white plumage is not an attractant for all wading bird species. White plumage may facilitate flock formation in certain species but does not serve as a universal attractant for wading birds of varying plumage colouration and size.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".