Estimating narwhal (<i>Monodon monoceros</i>) age using tooth layers and aspartic acid racemization of eye lens nuclei
Bibliographic record
Abstract
Abstract Counting growth‐layer groups (GLGs) in teeth is one of the most precise and widely accepted methods for aging marine mammals. Male narwhals have a large erupted tusk that can be used for aging, but this tusk is often difficult or expensive to obtain from hunters and most females do not display the tusk; thus, alternative methods for narwhal aging are needed. In this study, we aged narwhals by counting annual GLGs in embedded tusks and by measuring the change in the ratio of D‐ and L‐enantiomers of aspartic acid in the eye lens nucleus that occurs as the animal ages (the aspartic acid racemization [AAR] technique). Absolute age estimates were estimated for seven tusks aged ≤15 yr. Estimated age was a significant predictor of aspartic acid D/L ratios with a racemization rate ( K asp ) of 9.72 × 10 −4 /year ± 2.28 × 10 −4 and a (D/L) 0 of 3.46 × 10 −2 ± 1.78 × 10 −3 ( r 2 = 0.74). Results from our study, which included more younger GLG‐aged animals than previously evaluated, confirms AAR can be used to generate age estimates for narwhals.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 teacher head, 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".