Age estimation for young bowhead whales (Balaena mysticetus) using annual baleen growth increments
Bibliographic record
Abstract
We compiled age estimates and baleen plate δ 13 C data from 86 bowhead whales ( Balaena mysticetus L., 1758). We used previous whale age estimates based on aspartic acid racemization (AAR) and corpora counts to extend the use of δ 13 C data for age determination from cycle counting to a modified exponential model using annual baleen growth increments. Our approach used the growth increment data from individual whales in a nonlinear mixed effects model to assess both population-level and whale-specific growth parameters. Although age estimates from baleen-based models become less precise as the whales age, and baleen growth and length near steady state, the growth increment model shows promise in estimating ages of bowhead whales 10–13.5 m long with baleen lengths <250 cm, where other techniques are less precise or the data are scarce. Ages estimated using the growth increment data from such whales ranged from 6.4 to 19.8 years.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".