Another one bites the dust: Does incisor-arcade size affect mass gain and survival in grazing ungulates?
Bibliographic record
Abstract
Incisor-arcade size affects foraging efficiency in grazing ungulates and should be under strong selective pressure. We investigated individual variation in incisor-arcade size and its relationship with body mass and survival in bighorn sheep (Ovis canadensis) at Ram Mountain, Alberta, Canada, over 9 years. In adult ewes, incisor-arcade breadth and depth decreased with age, probably as a result of tooth wear. We found no effects of incisor-arcade size on survival of lambs or adult ewes. In adult ewes, an apparent positive effect of incisor-arcade size on survival disappeared when age was accounted for. Incisor-arcade breadth and depth had no effect on summer mass gain in lambs or adult ewes. Although linear models suggested that arcade breadth in lambs was correlated with summer mass gain, a latent variable path analysis model revealed that the correlation was due to an allometric relationship of arcade breadth with body size. Variation in incisor-arcade size in bighorn sheep appears to be due to individual variation in body size and age rather than to directional selection.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".