Manipulating territory size via vegetation structure: optimal size of area guarded by the convict cichlid (Pisces, Cichlidae)
Bibliographic record
Abstract
To test the predictions of optimal territory size models, we attempted to manipulate the size of area that a dominant convict cichlid fish (Archocentrus nigrofasciatus) would defend around a food patch by placing simulated vegetation at three different distances from the edge of the patch (0, 11, and 22 cm). As expected, the size of area defended against four smaller intruders increased as the vegetation was moved farther from the patch. Consistent with optimal territory size models, both the costs of defence, measured as chase radius and chase rate, and the benefits of defence, measured as the amount of food eaten by the defender, increased with the distance of the vegetation from the patch. Growth rates of the defenders, however, did not differ among the treatments, perhaps because the benefits of monopolizing food were balanced by the costs of defending a larger area. Our data support the hypothesis that the size of a guarded area around an ephemeral resource patch affects both the costs and benefits of defence.
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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 | 0.000 | 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".