The use of marine reserves in evaluating the dive fishery for the warty sea cucumber (<i>Parastichopus parvimensis</i>) in California, U.S.A.
Bibliographic record
Abstract
Management of sustainable fisheries depends upon reliable estimates of stock assessment. Assessment of many stocks is based entirely on fishery-dependent data (e.g., catch per unit effort), which can be problematic. Here we use fishery-independent data on stock size, collected within and outside of no-take reserves before and after the onset of fishing, to evaluate the status of the dive fishery for warty sea cucumbers, Parastichopus parvimensis, in southern California. Long-term monitoring data showed that abundance decreased throughout the Channel Islands within 36 years after the onset of fishing. No significant changes in the abundance of P. parvimensis were observed at the two non-fished reserve sites, although densities tended to increase following onset of the fishery. Beforeafter, controlimpact (BACI) analyses of seven fished and two non-fished sites implicated fishing mortality as the cause of 3383% stock declines. In sharp contrast, stock assessment based on CPUE data showed no declines and a significant increase at one island. To date, most discussion on marine reserves has focused on the protection and enhancement of exploited populations. Our study demonstrates the critically important, but often overlooked, role that marine reserves can play in providing reliable information on stock assessment.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".