Multi-indicator precautionary approach frameworks for crustacean fisheries
Bibliographic record
Abstract
Implementation of precautionary approach (PA) management systems has generally proven to be a betterment over historic management practices in sustainable use of fisheries resources. However, PA management systems can minimize the application of holistic assessment advice. Single-indicator approaches, typically featuring a measure of stock biomass or abundance, have emerged as dominant in developing methods for PA frameworks. This often leads to advice generated from these frameworks being applied within narrowly focused decision-making pathways and can minimize management application of important ancillary factors. We argue this outcome is counter to the intent of PA management systems and that multi-indicator PA frameworks are a better approach. In this analysis, we detail the multi-indicator PA system development for demonstrative case studies focused on three lucrative fisheries resources in Newfoundland & Labrador, highlighting how multi-indicator PAs are potentially a better approach for the management of stocks with a contrasting range of ecological roles, socioeconomic importance, and data quality. The work is intended to generate a discussion on how PA frameworks should evolve to best suit management purposes.
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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 | 0.051 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".