Sex-specific catch-up growth in the Texas field cricket,<i>Gryllus texensis</i>
Bibliographic record
Abstract
Periods of poor nutrition during an organism's development can negatively impact its adult fitness. If conditions improve, an organism may increase its growth rate (compensatory growth) or delay maturity to increase body size (catch-up growth). Heightened resource allocation to growth, however, could impair resource availability for other fitness-related traits. Because each sex maximizes fitness differently, there might be sex-specific responses to improving conditions. In this study, we investigated compensatory/catch-up growth and its sex-specific costs in a field cricket. After a 4-week period of poor-quality food, treatment crickets were switched to a good-quality diet until maturity. We predicted that males and females would respond to this diet change differently, as the importance of large body size differs between sexes. Contrary to our prediction, we found that neither male nor female crickets increased their growth rates after realimentation compared with controls. Despite a lack of compensatory growth, both sexes attained the same average body size, mass, and condition at adulthood as control individuals. Females achieved the same size as controls by delaying their maturation age (i.e. via catch-up growth) while males did not. Although the strategy used to catch-up differed between the sexes, its net effect on a suite of fitness-related traits was negligible in both sexes.
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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 | 0.000 |
| 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".