Use of otolith weight in length-mediated estimation of proportions at age
Bibliographic record
Abstract
Each year almost a million fish are aged from otoliths, primarily to estimate proportions at age for use in stock assessments. The preparation and reading of otoliths is time-consuming and thus expensive. Two techniques have been proposed to reduce costs. The first is length-mediated estimation, in which the length distribution from a large sample of fish is converted to an age distribution, using information (usually in the form of an age–length key) from a smaller sample containing length and age data. The second is to infer age from otolith weight (and/or other otolith measurements). These two cost-saving ideas are combined in a new method, length-mediated mixture analysis. It requires three samples – one with lengths only, one with lengths and otolith measurements, and one with lengths, otolith measurements and ages – and estimation is by maximum likelihood. The use of this method, which can be thought of as a generalisation of three established methods of age inference, is illustrated in two simulation experiments in a cost-benefit framework.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".