Valuing changes in frequency of fish stock assessments
Bibliographic record
Abstract
Updating stock assessments frequently and ensuring that the most recent fishery-dependent and -independent data are included is a costly endeavor. We use a management strategy evaluation for the mid-Atlantic summer flounder (Paralichthys dentatus) fishery to determine the economic returns to increasing update frequencies and decreasing the data management lag. We simulate the annual acceptable biological catch for the period 2015–2040 under a range of update frequencies and data lags. We calculate present value net economic benefits for the commercial and recreational fisheries for each scenario. Discounting, the timing of harvest quotas, species-specific price flexibilities, and fishing cost response to biomass and quota differences suggest that the benefits gained from frequent updating and reduction in data lags will vary by fishery. For summer flounder, we find the cost of more frequent updating (1 versus 5 years) and reducing the data management lag (1 versus 2 years) are more than compensated for by societal benefits generated by the fishery.
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".