Producer–Scrounger Games in a Spatially Explicit World: Tactic Use Influences Flock Geometry of Spice Finches
Bibliographic record
Abstract
Group‐foraging animals can either search for their food (producer) or search for opportunities to join the food discoveries of others (scrounger). To maximize food returns, producers should distance themselves from potential competitors whereas scroungers should increase proximity to potential producers. We investigated the extent to which playing one or the other tactic affected an individual’s location in captive flocks of ground‐feeding spice finches (Lonchura punctulata) as they foraged for hidden clumps of food on an aviary floor. We constrained some individuals to use the producer tactic by pre‐training them to find food hidden under lids. Constrained producers foraged significantly further from the center of flocks than constrained scroungers. Flocks with many scroungers were significantly more compact than flocks with fewer scroungers. The results are consistent with published simulations of spatially explicit producer–scrounger models and suggest that the use of producer and scrounger foraging tactics be included as a factor that affects an individual’s position within foraging groups.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".