Bibliographic record
Abstract
Abstract To understand the prevalence and conditional use of aggression among animals, one has to know its costs and benefits. The obvious cost of aggression in animals that possess teeth, claws or other specialized weaponry is injury. Many species, however, do not have such body parts and thus cannot readily injure others. The cost of aggression in these animals is not well studied. We tested whether aggression has a fitness cost in fruit flies, which can serve as a model species for animals without weapons that engage in aggression. In three experiments employing distinct protocols, we allowed focal flies to fight for control of an attractive food patch over 4 days and then compared their survivorship to that of flies not engaged in conflict. In all three experiments, fly survivorship was lower in the aggression than no‐aggression treatments. Microscopic examination revealed no differences in wing damage between flies of the aggression and no‐aggression treatments. The two most likely, non‐mutually exclusive explanations for lower survivorship post‐fighting are physiological changes due to stress, and metabolic alterations associated with a life‐history strategy optimized for high‐conflict settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".