Data from: The condition dependency of fitness in males and females: the fitness consequences of juvenile diet assessed in environments differing in key adult resources
Bibliographic record
Abstract
Variation in environmental or genetic quality leads to phenotypic variation in condition, but how much variation in fitness is created by this variation in condition? Using Drosophila melanogaster, we manipulated condition via alternative larval diets and then tested several key factors predicted to influence how much variation in fitness results from differences in condition. Specifically, we were interested in whether male and female fitness are affected equally by condition and whether the strength of selection on condition depends on the abundance of key resources limiting the reproductive output of each sex. We measured selection on condition in alternative assay contexts that varied in the abundance of adult food (a key resource for females) or in the abundance of females (a key resource for males). Overall, selection tended to be stronger on males than females. However, selection on males was weakened when the abundance of their key resource (females) was elevated. Increasing the abundance of the key resource for females (live yeast) elevated their reproductive output as expected but did not change the strength of selection in this sex. Instead, this manipulation increased selection on males, suggesting that this environmental factor indirectly affects selection on males via their interaction with females.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.028 |
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".