Improved estimation and forecasts of stock maturities using generalised linear mixed models with auto‐correlated random effects
Bibliographic record
Abstract
Abstract Estimation of proportion mature‐at‐age is an important component of the estimation of stock productivity. Current methods estimate maturity‐at‐age in each year or cohort independently. However, cohorts that are alive in the population at the same time are expected to experience similar conditions and therefore have similar maturity trajectories. Methods to take advantage of this information using binomial time series models with over‐dispersion are presented to improve the estimation of maturity‐at‐age, with applications to example populations of Atlantic cod, Gadus morhua Linnaeus, and American plaice, Hippoglossoides platessoides (Fabricius). An auto‐correlated beta‐binomial model fit the data well compared to other models investigated and improved forecasts of maturities for both populations. Use of an auto‐correlated beta‐binomial model should lead to improvements in projections of stock status and, hence, improvements in fisheries management advice.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".