Bibliographic record
Abstract
Management strategies must be designed to take into account the uncertainty inherent in fish populations and their assessments. Annual recruitment variation is an important component of uncertainty. Several methods that allow the estimation of annual recruitment in statistical catch-at-age models are described: (a) maximum likelihood estimation with no penalty on the annual recruitment residuals, (b) maximum likelihood estimation with a lognormal penalty on the annual recruitment residuals, (c) importance sampling to numerically approximate the marginal likelihood with a lognormal penalty on the annual recruitment residuals, and (d) full Bayesian integration using Markov Chain Monte Carlo with a lognormal prior on the annual recruitment residuals. Simulation analysis is used to test the performance of these methods. All four methods perform similarly at estimating quantities that are based on averaging or summing multiple estimates of annual recruitment; however the marginal likelihood method (c) and Bayesian integration (d) perform best at estimating annual recruitment and the standard deviation in annual recruitment residuals (σR) when catch-at-age data is missing for some years. The ability to estimate σR can be important for defining uncertainty when developing management strategies. The methods are applied to a New Zealand snapper (Pagrus auratus) stock and the estimate of σR is approximately 0.6.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".