Factors that affect detection of threats from food competitors within a group
Bibliographic record
Abstract
Early detection is crucial to avoid threats from predators and competitors. Factors that affect threat detection have rarely been investigated in the context of food competition. I examined the ability to detect threats of displacement from feeding areas by competitors in flocks of staging Semipalmated Sandpipers (Calidris pusilla (L., 1766)). I predicted that targeted birds would be more likely to detect competitors attacking from the side or from the front rather than from behind because a blind area behind the head interferes with detection. Preening or sleeping birds in feeding areas might be less likely to detect attacks if such activities interfere with vigilance. If targeted birds maintain vigilance against attackers, attacks launched from farther away should be detected more frequently since more time is available for detection. As predicted, attacks were detected less often when launched from behind and more often when launched from farther away. The longest attacks were detected less often perhaps because birds relaxed their vigilance when neighbours were farther away. The ability to detect threats did not vary with targeted bird activity. Several factors influence the ability to detect threats from food competitors providing us with a novel context in which to investigate threat detection.
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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 | 0.002 |
| 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 | 0.001 | 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".