Phenotypic Correlates of Scrounging Behavior in Zebra Finches: Role of Foraging Efficiency and Dominance
Bibliographic record
Abstract
Abstract In flocks, individuals can search for their own food using the producer tactic or exploit the discoveries of companions using the scrounger tactic. Models of the producer–scrounger game usually assume that tactic payoffs are independent of individual phenotypic traits. However, factors such as dominance status or foraging efficiency may constrain the use of tactics and lead to asymmetric tactic use among individuals. For instance, in flocks composed of foragers with unequal foraging efficiency, foragers that are less efficient at obtaining food are expected to rely on the scrounger tactic to a greater extent. I examined the role of foraging efficiency and dominance status as potential correlates of scrounging behavior in small aviary flocks of zebra finches (Taenopygia guttata). Individual foraging efficiency was documented in each flock in a treatment that prevented scrounging. In a subsequent treatment that allowed scrounging, higher levels of scrounging occurred as predicted in foragers with lower foraging efficiency. Dominance status was a poor predictor of tactic choice. Birds that arrived later on the foraging grid foraged less efficiently when scrounging was prevented and used scrounging to a greater extent when allowed, suggesting a link between boldness, foraging efficiency and the choice of foraging tactics in small flocks of zebra finches.
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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 | 0.000 | 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".