The role of sex and temperature in melanin-based immune function
Bibliographic record
Abstract
Sex differences in immunity have been observed across a wide range of species. Still, it remains unclear how sex-specific interactions with the environment are linked to sex differences in immunity. We studied the plasticity of immunological sex differences by focusing on melanin-based traits in the Pacific field cricket (Teleogryllus oceanicus (Le Guillou, 1841)). Insects rely on the pigment melanin for both immune function and coloration of the cuticle; therefore, changes in melanin production for one of these traits may indirectly affect the other. Male crickets use melanized wing structures to chirp. These cuticular structures are missing in females and a songless male morph. Given that the thermal environment influences cuticle melanization, we investigated the interactive effects of sex and developmental temperature on melanin-based immunity. Both immunity and wing cuticle melanism were reduced in individuals that developed under warmer temperatures. Rearing temperature also mediated the extent to which the sexes differed in immune traits. Males had darker cuticles, whereas females expressed greater immune activity, suggesting that sex-specific investment in melanin corresponds with sex differences in immunity. However, the lack of immunological differences between the two male morphs does not support the hypothesis that investment in cuticle melanism affects investment in immunity.
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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".