Monopolization of food by zebrafish (<i>Danio rerio</i>) increases in risky habitats
Bibliographic record
Abstract
Dominant zebrafish (Danio rerio) previously have been shown to reduce their monopolization of food when foraging in structurally complex habitats compared with open habitats. Complex habitats may be more difficult to defend but may also be safer. To decouple these effects, we compared aggression and monopolization of food in groups of zebrafish foraging in an open habitat and one with overhead cover, as well as in an open habitat and a complex (vegetated) habitat. Covered and open habitats should have been equally defendable. In our experiments, fish used covered habitats more than open ones, suggesting that the perceived risk of predation was lower in covered habitats. There was no difference in use of vegetated and open habitats, suggesting that these habitats, which should differ in defendability, did not differ in safety. We found that the degree of food monopolization (expressed in the coefficient of variation within groups) at risky feeders was significantly greater in open habitats than in covered, but not vegetated, habitats. We did not find a difference in aggression between habitats. These results indicate that resource monopolization in groups of zebrafish is greater in risky habitats and support the hypothesis that the lower monopolization of food in complex habitats could result from greater safety in those habitats rather than, or in addition to, the reduction in defendability.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".