How do marine closures affect the analysis of catch and effort data?
Bibliographic record
Abstract
Fishery managers increasingly use marine closures as a tool to conserve ecosystems, biodiversity, and fish abundance. Despite the suggested benefits of closed areas, the limited or no data collection within them leads to difficulties assessing the population status. We investigated how spatial closures impacted the reliability of indices of abundance obtained from standardization methods applied to catch per unit effort data. The presence of closed areas generally introduced a bias in the derived index of abundance, and the magnitude of bias increased as the portion of the population in closed areas increased. In general, restricting the data to the areas that have been continuously fished over time performed best when spatial closures protected a small to medium portion of the population. However, as the portion of the population that was protected increased, the time series bias associated with this approach increased, and the use of an imputation approach was needed for adequate performance. Similarly, the collection of ancillary data in the closed area reduced bias in the estimate of final year depletion when area closures protected a large portion of the population.
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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 | 0.198 | 0.620 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".