Competition for food in 2 populations of a wild-caught fish
Bibliographic record
Abstract
Aggressive behavior when competing for resources is expected to increase as the ratio of competitors-to-resource ratio (CRR) units increases. Females are expected to be more aggressive than males when competing for food when body size is more strongly related to reproductive success in females than in males, whereas aggression is predicted to decrease under high ambient predation risk by natural selection. Under the risk allocation model, however, individuals under high ambient predation risk are expected to be more aggressive, and forage more in the absence of imminent risk than their low risk counterparts. An interaction between adult sex ratio (i.e., adult males/females), ambient predation risk (high vs. low), and sex on intrasexual competition for mates in Trinidadian guppies Poecilia reticulata has been shown. The interaction suggested an increase in aggression rates as CRR increased, except for males from the high predation population. To compare the patterns of competition for food versus mates, we replicated this study by using food patches. We allowed 4 male or 4 female guppies from high and low predation populations to compete for 5, 3, or 1 food patches. The foraging rate was higher in a high rather than low ambient predation risk population. Surprisingly, CRR, sex, and population of origin had no effect on aggression rates. Despite other environmental differences between the 2 populations, the effect of ambient predation risk may be a likely explanation for differences in foraging rates. These results highlight the importance for individuals to secure food despite the cost of competition and predation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".