The impact of regime shifts on the performance of management strategies for the Gulf of Alaska walleye pollock (Theragra chalcogramma) fishery
Bibliographic record
Abstract
Climate variability affects marine ecosystems. The mechanisms relating low-frequency environmental fluctuations (regime shifts) and their impacts on marine populations are not well established for most species, but there is an expectation that the accuracy of scientific advice provided to fisheries management would be improved if management strategies incorporated the effects of regime shifts on stocks. Management strategy evaluation is used to examine the impact of regime shifts in average recruitment on the performance of management strategies for the fishery for walleye pollock, Theragra chalcogramma , in the Gulf of Alaska. The current and four alternative management strategies are evaluated. The alternatives include management strategies with different definitions of the average recruitment used when calculating management reference points, e.g., a 25-year sliding-window method and a “dynamic B 0 ” method. The current management strategy, which ignores the possibility of future regime shifts, kept the spawning biomass higher relative to the target level than the other management strategies and had the lowest risk of fishing mortality exceeding the overfishing limit. The sliding-window management strategy achieved the highest catches and the lowest interannual variation in catch, although at the cost of a higher risk of the fishing mortality exceeding the overfishing limit.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".