Quantifying and comparing male harm between two Drosophila serrata lab populations
Bibliographic record
Abstract
Male harm arises when traits that increase reproductive success in competition with other males also harm females as a side-effect. The extent of harm depends on male and female phenotypes, both of which can diverge between populations. Within a population, harm is inferred when increased exposure to males reduces female fitness, but studies of the divergence of male harm rarely manipulate male exposure. Here, we quantify male harm and compare its magnitude between two lab populations of Drosophila serrata that were derived from a common ancestor seven years earlier and subsequently held under conditions that minimized environmental differences. We manipulate female exposure to males in a factorial design involving all four combinations of males and females from these populations, providing insight into divergence in both sexes. Our results reveal substantial harm to females and provide stronger evidence of divergence in males than in females. Using these and other published data, we discuss conceptual issues surrounding the quantification and comparison of harm that arise because it involves a comparison of multiple quantities (e.g., female fitness under varying male exposure), and we demonstrate the increased insight that is gained by manipulating male exposure to quantify these.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".