Range-wide contrast in management outcomes for transboundary Northeast Pacific sablefish
Bibliographic record
Abstract
Sablefish ( Anoplopoma fimbria) of the Northeast Pacific support a highly mobile, valuable fishery resource currently managed as three separate populations. Recent work has shown sablefish to be genetically mixed; have high movement rates; and have synchronous biomass trends, including recent declines. A management strategy evaluation was developed with stakeholders and scientists from three regions to investigate whether spatially structured management paradigms might result in better conservation and economic outcomes. The management strategy evaluation includes a transboundary operating model to represent spatial population dynamics including movement and a delay–difference estimation method with varying spatial complexities and potential stratifications, and harvest control rules. Mismatches in the spatial scale of management and the underlying biological units pose a crucial risk of localized depletion in the southern U.S. West Coast. This study presents one of the first transboundary, spatially-explicit management strategy evaluations conditioned to actual data. These results underscore the importance of spatial management strategy evaluation tools and implications when regional management is conducted in isolation. Future work should incorporate additional spatial hypotheses and investigate the drivers of recruitment patterns range-wide.
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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 | 0.004 |
| 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 | 0.000 | 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".