Development of a stock-specific management procedure for snow crab fishery in the Sea of Japan using conditioned surplus production models in a data-moderate situation
Bibliographic record
Abstract
A management procedure (MP) automatically calculates an acceptable biological catch from input data, and its performance is quantitatively evaluated by closed-loop simulations of population dynamics with their uncertainties (operating models). In snow crab in the Sea of Japan, an MP based on survey biomass estimates was adopted by stakeholders, rather than a generic MP recommended for data-moderate Japanese stocks. This was done without quantifying its performance owing to data and resource limitations. This study evaluated the performance of the existing MPs and modified the generic MP through closed-loop simulations with state–space surplus production models (SSPMs) conditioned with actual data. The survey-based MP generally worked well to achieve sustainable catch and biomass but tended to be sensitive to the bias in biomass estimates. The modified MP can be more robust to the bias than the survey-based MP with higher catches than the generic MP. This study demonstrates that, although continuing efforts of collecting composition data are inevitable, SSPM could be used as the conditioned operating models even in data-limited situations to sufficiently represent the stock status and its uncertainty.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".