Optimizing Fishing Quotas to Meet Target Fishing Fractions of an Internationally Exploited Stock of Pacific Sardine
Bibliographic record
Abstract
Abstract Two stocks of Pacific Sardine Sardinops sagax migrate seasonally and synchronously along the west coasts of Mexico, the USA, and Canada. Landings from the two stocks are currently combined in U.S. assessments of the northern stock, but the stocks may be differentiated by their associated seawater habitats, which are predominantly characterized by different ranges of sea surface temperature. We compared the combined and temperature-differentiated landings of the two stocks in each country for the period 1993–2011, demonstrating how different attributions of the landings affected the estimated annual fishing fraction (F) for the northern stock. Using combined or stock-differentiated landings and assessed biomasses, we found that the current harvest control rule (HCR) for Pacific Sardine has not consistently maintained a total F below the U.S. target value because the “distribution” parameter (used to account for the northern stock's proportion in the U.S. Exclusive Economic Zone [EEZ]), has not adequately accounted for northern stock landings in Mexico and Canada. We propose a refinement to the HCR, giving explicit consideration to the summed landings in Mexico and Canada, to more optimally set the annual U.S. quota. The performance of our method was compared with (1) the values of F that would have been achieved during the federal management period (2000–2011) if the U.S. quotas had always been met and (2) the generally lower actual values of F that were calculated using the default HCR formulation (1993–2011). We demonstrate that application of our method would permit more U.S. fishing for Pacific Sardine when the northern stock is large and predominantly located in the U.S. EEZ and would curtail U.S. fishing when a large proportion of the stock is present and fished in the Mexican EEZ, Canadian EEZ, or both. Received December 12, 2013; accepted July 16, 2014
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".