Benefits and drawbacks of determining reproductive histories for black bears (<i>Ursus</i> <i>americanus</i>) from cementum annuli techniques
Bibliographic record
Abstract
Recruitment is difficult to estimate but is essential for determining population trend. Recruitment in bears can be estimated from patterns in width of cementum annuli that indicate years with cubs. We evaluated reproductive history estimates from cementum annuli of 19 101 black bears (Ursus americanus Pallas, 1780) collected over 20 years to determine the benefits and drawbacks of this technique for management agencies. The technique only worked to estimate reproductive histories for 25% of submitted samples, and 49% of samples with estimates contained uncertain litters. Whether uncertain litters were counted or not caused significant variation in estimates of age at first litter, number of litters per female, and interbirth intervals. Hence, naive treatment of uncertain litters may bias analyses. A data set we optimized to reduce bias showed that litters per female ranged from 0 to 12, mean interbirth interval was 2.07 years, and both increased as females aged. Large samples of teeth collected from harvested bears over multiple decades potentially provides a wealth of information on reproductive parameters at a minimal cost compared with intensive field studies, but until uncertain litters are understood mechanistically and can be better quantified, reproductive estimates from this technique should be interpreted with caution.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".