Aggressive behaviours, food deprivation and the <i>foraging</i> gene
Bibliographic record
Abstract
A pleiotropic gene governs multiple traits, which might constrain the evolution of complexity due to conflicting selection on these traits. However, if the pleiotropic effect is modular, then this can facilitate synergistic responses to selection on functionally related traits, thereby leveraging the evolution of complexity. To understand the evolutionary consequence of pleiotropy, the relation among functionally different traits governed by the same gene is key. We examined a pleiotropic function of the foraging ( for ) gene with its rover and sitter allelic variants in fruit fly, Drosophila melanogaster . We measured for 's effect on adult male aggressive behaviours and whether this effect was shaped by for 's known role in food-related traits. Rover exhibited higher levels of offensive behaviour than sitters and s2, a sitter-like mutant on rover genetic background. With a Markov chain model, we estimated the rate of aggression escalation, and found that the rover pattern of aggressive escalation more rapidly intensified fights. Subsequent analysis revealed that this was not caused by for 's effect on food-related traits, suggesting that for might directly regulate aggressive behaviours. Food deprivation did not elevate aggression, but reduced intermediate-level aggressive behaviours. Aggression and other foraging-related behaviour might comprise a synergistic trait module underlaid by this pleiotropic gene.
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 | 0.000 |
| 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".