Aggressiveness and Size: A Model and Two Tests
Bibliographic record
Abstract
Individual variation in aggressive behavior in animals might be caused by adaptive covariation with body size. We developed a model that predicts the benefits of aggressiveness as a function of body size. The model indicated that individuals of intermediate sizes would derive the greatest benefits from being aggressive. If we assume that the cost of aggression is approximately uniform with respect to body size, selection should favor higher aggression in intermediate-sized individuals than in large or small individuals. This prediction was tested by stimulating male Madagascar hissing cockroaches, Gromphadorhina portentosa, with disembodied antennae and recording the males' aggressive responses. Antennae from larger males evoked weaker responses in subjects, suggesting that males obtained information about their opponents' size from the opponents' antennae alone. After accounting for this effect, we found support for the key prediction of our model: aggressiveness peaked at intermediate sizes. Data from actual male-male interactions validated that the antenna assay accurately measured aggressiveness. Analysis of an independent data set generated by staging male-male interactions also supported the prediction that intermediate-sized males were most aggressive. We conclude that adaptive covariation between body size and aggressiveness explains some interindividual variation in aggressiveness.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".