Wild Carib grackles play a producer scrounger game
Bibliographic record
Abstract
Producer–scrounger (PS) game-theoretical foraging models make predictions about the decision of group-feeding animals either to look for food (produce) or for opportunities to exploit the discoveries of other foragers (scrounge). We report the most complete demonstration to date of the applicability of the PS foraging game in a free-living animal, the Carib grackle (Quiscalus lugubris) of Barbados. As assumed by PS games, the payoffs obtained by scroungers were negatively frequency dependent. Experimentally, increasing the cost of scrounging led to a decrease in the observed proportion of scroungers, whereas raising the cost of producing increased the proportion of scroungers. Observations of marked birds revealed that group-level changes could be brought about by individual flexibility in tactic use. Despite consistent individual differences in tactic use, most birds used both tactics and could alter their use of producing and scrounging when conditions changed. We found no difference in the payoffs obtained by producers and scroungers, suggesting a symmetrical game equilibrium. Our results call for testing the PS foraging game in a broader range of biological systems that include different types of scrounging behavior (e.g., scramble, stealthful, or aggressive scrounging) as well as the exploitation of different phases of food production (e.g., searching, handling).
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".