Bibliographic record
Abstract
Several models of vigilance assume that it can be adjusted in response to levels maintained by companions. However, the assumption of visual monitoring of vigilance has received little empirical scrutiny. Various treatments in a laboratory study manipulated the ability of zebra finches (Taenopygia guttata) foraging in pairs to monitor visually the vigilance of companions. In one treatment, the insertion of an uncovered partition allowed members of a pair to monitor, if needed, the vigilance of their companion. The addition of a partial cover to the partition in a further treatment eliminated any visual monitoring of vigilance by companions on either side of the partition while leaving the view from the rest of the cage unaltered. The final treatment, without a partition, allowed the feeding behaviour of unrestricted birds to be examined. As birds spent most of their time feeding or vigilant, feeding rate was considered a proxy for vigilance. After an initial difference in feeding rate across treatments, related perhaps to the novelty associated with the insertion of a partition, birds obtained food at a similar rate whether visual monitoring of vigilance was possible or not and whether individuals were physically separated or not. In agreement with recent empirical and theoretical findings, the study provides little evidence for visual monitoring of vigilance in birds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".