Adjusting for bias due to variability of estimated recruitments in fishery assessment models
Bibliographic record
Abstract
Integrated analysis models provide a tool to estimate fish abundance, recruitment, and fishing mortality from a wide variety of data. The flexibility of integrated analysis models allows them to be applied over extended time periods spanning historical decades with little information from which to estimate the annual signal of recruitment variability to modern periods in which more information about recruitment variability exists. Across this range of data availability, the estimation process must assure that the estimated log-normally distributed recruitments are mean unbiased to assure mean unbiased biomass estimates. Here we show how the estimation method implemented in the integrated analysis model, Stock Synthesis, achieves this unbiased characteristic in a penalized likelihood approach that is comparable to the results from Markov chain Monte Carlo. The total variability in recruitment is decomposed into variability among annual recruitment estimates based on information in the data and a residual variability. Because data are never perfectly informative, we show that estimated recruitment variability will always be less than the true variability among recruitments and that the method implemented here can be used to iteratively estimate the true variability among recruitments.
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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 | 0.041 | 0.173 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".