A general framework for integrating the standardization of catch per unit of effort into stock assessment models
Bibliographic record
Abstract
A general framework is presented for integrating the standardization of catch per unit of effort (CPUE) into stock assessment models. Catchability is modeled using both continuous and categorical explanatory variables. The likelihood for the CPUE data is combined with the other likelihoods from the stock assessment model; the parameters used to model catchability are estimated simultaneously with the other parameters of the stock assessment model. The method is applied to a New Zealand rock lobster (Jasus edwardsii) stock, and the results are compared with those obtained using a generalized linear model. The point estimates are similar for both methods, but the confidence intervals from the integrated framework are much narrower. Simulation analysis supports the findings that the integrated approach gives narrower confidence intervals that more accurately represent the uncertainty in the parameter estimates, provided the model is correct.
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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 | 0.040 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".