Zebra finches in poor condition produce more and consume more food in a producer–scrounger game
Bibliographic record
Abstract
When they forage in groups, animals can search for their own food (producer tactic) or exploit the discoveries of others (scrounger tactic). Previous experimental inquiries have demonstrated that individuals vary in their tendency to play either tactic but the extent to which individual factors influence variation in foraging behavior are little studied. In the present study, we have assessed the influence of natural variation in body condition on the differential use of social foraging tactics and their resulting payoffs in zebra finches (Taeniopygia guttata). The producer tactic was found to yield more consistent and predictable rewards across trials than the scrounger tactic. The use of producer was related to reduced variation in food intake and an increased amount of food consumed. We found that poor-condition birds were more likely to produce and so consumed more seeds than good-condition birds. The results are consistent with theoretical models of variance-sensitive social foraging but suggest that scrounging may not represent a variance-averse option in all situations. We propose that in a producer–scrounger context, the variance-averse option may depend on group size and food clumping. Finally, we discuss our results in relation to interindividual differences in metabolism and behavior.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".