Sexual size dimorphism in bighorn sheep (<i>Ovis canadensis</i>): effects of population density
Bibliographic record
Abstract
Sexual dimorphism is an important characteristic of many mammals, but little is known about how environmental variables may affect its phenotypic expression. The relationships between population size, body mass, seasonal mass changes, and sexual mass dimorphism were investigated using 22 years of data on individually marked bighorn sheep (Ovis canadensis) on Ram Mountain, Alberta. The number of adult ewes was artificially maintained low from 1972 to 1981 and then allowed to increase. The body mass of males from 0 to 7 years of age was negatively affected by population density. Female body mass was negatively affected by population density up to 2 years of age. As the number of ewes increased, sexual mass dimorphism of sheep aged 27 years declined. Population density had a negative effect on seasonal mass changes of young males and females. Density also had a weak but significant positive effect on yearly mass gain of 2-year-old females, suggesting compensatory growth. Females appear to compensate for resource shortages early in life, while males show a lifelong negative effect. We suggest that these sexual differences are due to the greater flexibility of resource allocation to growth or reproduction by females than by males.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".