Estimating population age structure using otolith morphometrics: a test with known-age Atlantic cod (Gadus morhua) individuals
Bibliographic record
Abstract
Traditional age reading is a rather subjective method that lacks true reproducibility, producing ageing error that propagates up to stock assessment. One alternative is represented by the use of otolith morphometrics as a predictor of age. An important issue with such a method is that it requires known-age fish individuals. Here we used known-age Atlantic cod ( Gadus morhua ) from the Faroe Bank and Faroe Plateau stocks. Cod populations usually show quite large variation in growth rates and otolith shape. We showed that including otolith morphometrics into ageing processes has the potential to make ageing objective, accurate, and fast. Calibration analysis indicated that a known-age sample from the same population and environment is needed to obtain robust calibration; using a sample from a different stock more than doubles the error rate, even in the case of genetically highly related populations. The intercalibration method was successful but generalization from one stock to another remains problematic. The development of an otolith growth model is needed for generalization if an operational method for different populations is required in the future.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".