Nutrition and ontogeny influence weapon development in a long-lived mammal
Bibliographic record
Abstract
Selection in male cervids should optimize allocation of nutritional resources to the competing demands of body growth versus weapon development. We investigated allocation decisions of growing and mature male white-tailed deer (Odocoileus virginianus (Zimmerman, 1780)) from three regions of low, moderate, and high diet quality. We tested (i) if deer under greater nutritional limitations would allocate proportionally less to antler growth, (ii) if antler and body mass became less variable with age, and (iii) if antler size consistently exhibited positive allometry with body mass across age classes and nutritional planes. Greater nutrition increased antler allocation in 2.5 to 4.5 year olds but not in yearlings or prime-aged males. Variability of antler mass decreased with age and was generally less in more fertile regions, but body mass was equally variable across all ages and regions. Antler mass was positively allometric with body mass for all combinations of age class and region but exhibited age- and region-related differences. Our results suggest that accruing body mass is more important to lifetime reproductive success than increasing weapon size. Reduced allometric coefficients in older males likely stem from increasing use of skeletal mineral reserves, selective pressures favoring greater body mass, and possible selection for optimal weapon strength and structure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".