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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.030 | 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".