Persistence and aggression in Drosophila melanogaster
Bibliographic record
Abstract
All animals face a complex environment full of obstacles that they must overcome in order to survive and reproduce. How an individual responds to its environment is essential to overcoming such obstacles in order to maximize fitness. In my thesis, I focused on the roles of persistence and aggression in achieving fitness-relevant goals. Persistence is continuing in a course of action in spite of difficulty or resistance, and aggression is any instance where an individual uses physical, and potentially damaging, force against a conspecific. I used fruit flies (Drosophila melanogaster) as a model system to examine the ways in which males use persistence and aggression to attain fitness-relevant goals such as defending resources, gaining access to females, and mating. I first examined how a male’s age affected his persistence in courting recently mated females, who are generally unreceptive, and found that older males were more persistent than younger males (Chapter 2). Next, I showed that males of different ages differed in their courtship persistence in the presence of a competitor, and that males were able to subtly, but directly, interfere with one another’s courtship attempts (Chapter 3). I then demonstrated how males were able to use aggression in a mate guarding context to reduce the likelihood that a competitor male mated with their recent mate (Chapter 4), and as a form of resource defense to defend a desirable food patch in the presence of a potential mate (Chapter 5). Finally, I considered male persistence in the pursuit of unreceptive females as a form of male sexual aggression towards recently mated and sexually immature females (Chapters 5 and 6). Overall, my thesis work demonstrates how complex, and sometimes intertwined, the roles of persistence, aggression, and sexual coercion can be even within a ‘simple’ model organism, such as the fruit fly.
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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 | 0.000 |
| 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".