Selection and estimation of sequential catch-at-age models
Bibliographic record
Abstract
Fish stock assessment by catch-at-age and survey data is affected by many stochastic elements: measurement errors; sampling variations; natural variations in mortality, catchability and migrations; technological and social effects on fishing intensity and selectivity. Estimation of simulated models shows that the bias in estimation by linear approximation of the Kalman filter or automatic approximation of the marginal likelihood function is much smaller than the errors produced by the stochastic elements. In time series modelling, they are represented by residuals in the equations. Strong simplifying assumptions about these effects are common in catch-at-age analysis, but estimation of models for Icelandic cod ( Gadus morhua ) and pollock ( Pollachius virens , herein referred to as saithe) demonstrates that the relative importance of different random elements can vary greatly between stocks. These assumptions include exact catch-at-age measurements, no irregular migrations or variations in natural mortality, separable fishing mortality rates, and no permanent variations in survey catchability. Inappropriate simplificactions can have a strong effect on stock estimates. It is possible and important to test simplifying assumptions by comparison with more general models. Estimation of the magnitude of natural mortality is also examined.
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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 | 0.005 | 0.019 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".