MAJOR-HISTOCOMPATIBILITY-COMPLEX-ASSOCIATED VARIATION IN SECONDARY SEXUAL TRAITS OF WHITE-TAILED DEER (ODOCOILEUS VIRGINIANUS): EVIDENCE FOR GOOD-GENES ADVERTISEMENT
Bibliographic record
Abstract
Good-genes hypotheses predict that development of secondary sexual characters can be an honest advertisement of heritable male quality. We explored this hypothesis using a cervid model (adult, male white-tailed deer, Odocoileus virginianus) to determine whether antler development could provide an honest signal of a male's genetic quality and condition to adversaries. We compared antler, morphometric, hormonal, and parasitic data collected from hunter-harvested deer to characteristics of the Mhc-DRB (Odvi), the most widely studied gene of the major histocompatibility complex (MHC) in Artiodactyla. We detected associations between genetic characteristics at Odvi-DRB and antler development and body mass, suggesting that antler development and body mass may be associated with pathogen resistance in deer and thus may be an honest signal of genetic quality. We also detected associations between Odvi-DRB characteristics and serum testosterone during the breeding season, suggesting that certain MHC characteristics may help deer cope with stresses related to breeding activity. In addition, we observed a negative relationship between degree of antler development and overall abundance of abomasal helminths. Our observations provide support for the hypothesis that antler development in white-tailed deer is an honest signal of quality.
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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".