The interim management procedure approach for assessed stocks: Responsive management advice and lower assessment frequency
Bibliographic record
Abstract
Abstract Stock assessments are often used to provide management advice, such as a total allowable catch (TAC), to fishery managers. Many stocks are not assessed annually, and the TAC from the previous assessment is often maintained in years between assessments. We developed two interim management procedures (MPs) that update the estimate of current vulnerable biomass from a surveyed index of abundance to adjust the TAC from a previous assessment. These MPs differ in how they handle uncertainty in observed indices. Using closed‐loop simulation, we evaluated the two interim MPs (with 10‐ and 5‐year assessment intervals) against several “status quo” approaches: (1) an annual assessment, and (2) a stock assessment every 5 or 10 years with (a) fixed TACs or (b) projections between assessments. We evaluated performance across three life‐history types and six operating model scenarios. The interim MPs performed similarly to annual assessments in terms of trends in biomass and yield, regardless of the assessment interval of the interim MPs. The interim MPs often produced more yield than the Fixed TAC MP with 10‐year assessment intervals, for example, in depleted scenarios. The Fixed TAC MP performed more similarly to interim MPs when the assessment interval for the Fixed TAC MP was decreased to five years. The interim MPs can also perform well when circumstances arise that are not accounted for in the Projection MP. Our results show that interim MPs should be considered for infrequently assessed stocks or rebuilding stocks, and highlight potential cost savings of interim MPs over annual assessments.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".