A simulation-based approach to assess sensitivity and robustness of fisheries management indicators for the pelagic fishery in the Bay of Biscay
Bibliographic record
Abstract
Indicators are widely promoted as means to monitor ecosystem status or to evaluate fisheries management performance. “Which indicators are most relevant as decision-support tools in fisheries management?” still remains a topical question. Indicators should be metrics related to fish populations and fleets and should be sensitive to management strategies. However, given the complexity of the processes involved, it is often difficult to unequivocally interpret variations in metrics. A simulation approach was used to study metric properties and to identify robust and relevant fishery indicators. By applying sensitivity analysis methods, simulation designs were built that cross a variety of management scenarios and uncertainty hypotheses. Bio-economic outputs were simulated using a mechanistic model (ISIS-Fish), and their properties were statistically analyzed. This approach was applied to the pelagic fishery of the Bay of Biscay. The analysis of metric properties highlighted the major factors driving variations in each metric and identified the important sources of uncertainty that need to be reduced to allow the use of metrics as indicators. Although very few metrics gave robust indications of management performance, sensitivity indices evidenced how management performances could be improved, and spatially disaggregated metrics provided insights into the mechanisms underlying management performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".