The potential use of environmental information to manage squid stocks
Bibliographic record
Abstract
Most commercially exploited squid species have short life cycles and stocks composed of recruits from a single cohort, the size of which is unknown prior to the fishing season. Recent studies suggest that strong environmentrecruitment relationships may exist for a number of squid stocks. Using simulation models based on Falkland Island Patagonian squid (Loligo gahi), the recruit abundance of which is predicted by sea-surface temperature, we propose a method for using predictive relationships in the management of squid populations. We compare a management strategy based on recruitment prediction with historical data from the fishery, which was managed in the absence of these predictions. Our results suggest that varying effort on the basis of an environmental correlate of recruitment can reduce the risk of not meeting conservation targets while increasing yield. Effort has to be reduced in years of low abundance but licensing additional effort in years of high abundance increases long-term average catches. Even if effort levels were not allowed to vary by more than 50% between years, a management strategy for L. gahi based on prediction would have resulted in higher average catches and a reduced probability of the stock biomass falling below a notional conservation limit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".