Estimating Cementum Annuli Width in Polar Bears: Identifying Sources of Variation and Error
Bibliographic record
Abstract
Distinct annuli in cementan, a mineralized tissue surrounding the root of mammalian teeth, are used to estimate age in wildlife. Life-history information may be recorded in cementum patterns but interpretation is complicated by variation in cementum width between individuals, among their teeth, and around the surface of the root. First premolar teeth from polar bears (Ursus maritimus) were evaluated. We identified sources of variation in cementum growth and methods are presented that reduce error and permit comparisons within and between individuals. A minimum of 10 measurements from 1 aspect was required to produce precise estimates of cementum growth layer group (GLG) width. Variance component analysis revealed that comparisons between distal and mesial aspects of the root introduced the greatest variation among bears. Controlling for aspect, variance was partitioned differently between the mesial and distal surfaces. Comparisons between maxillary and mandibular premolars from the same bear indicated that data from these teeth should not be pooled; data collected from left and right lower premolars may be combined. Indices to represent adjusted GLG widths are described that reduce age and allometric effects, allowing life-history or environmental factors to be compared.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".