Body length and mass growth of the brown bear (<i>Ursus arctos</i>) in northern Canada: model selection based on information theory and ontogeny of sexual size dimorphism
Bibliographic record
Abstract
We compared four nonlinear growth functions in modeling body length and mass size-at-age data for the brown bear ( Ursus arctos L., 1758) in northern Canada of wide-ranging body sizes and ages. Then, we analyzed the sex differences in patterns of growth and ontogeny of sexual dimorphism in this species revealed by the best model from these alternatives. The von Bertalanffy function proved to be the most parsimonious model because it was easy to fit, with higher fitting degrees, lower root mean squared standard deviation of data points about fitted growth curve, larger Akaike weight, and fewer parameters derived directly from metabolic laws that accurately estimated the observed body length and mass growth profiles. Our growth models indicated an association between sexual growth divergence and the onset of reproduction in females, together with more rapid and prolonged male growth. These findings suggest that sexual size dimorphism develops in part by constraints on female growth from high energetic costs of reproduction. In contrast, males do not experience a comparable energetic trade-off after reaching sexual maturity and apparently allocate available energetic resources to growing faster and longer to produce larger body size, which benefits more competitive males in terms of increased reproductive success.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".