What to do when you have almost nothing: A simple quantitative prescription for managing extremely data‐poor fisheries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The cost, complexity and the lack of technical capacity in many countries have made the scientific assessment and sustainable management of data‐poor fisheries a persistent problem. New and innovative approaches are needed to stop the ongoing decline of data‐poor fisheries and loss of coastal biodiversity they are driving. In recent decades, marine protected areas have become the most preferred form of management for study and have been widely implemented as broadly applicable powerful management tools for data‐poor fisheries, but although clearly capable of building biomass within sanctuaries, their effectiveness for sustaining fisheries is proving more difficult to substantiate. This study suggests the new approach needed is actually a return to the established basics of managing size selectivity. Previous studies have established the wisdom of managing size selectivity and fishing pressure to catch fish above the size or age of maturity, but their prescriptions are difficult to implement without age studies, or the capacity for controlling catches and fishing pressure. This study develops an easily implementable rule of thumb based simply on multiples of size of maturity and quantifies its benefit where controlling fishing pressure is not yet possible. Our study provides a timely reminder that even if used alone, size selectivity, the oldest form of management, still produces pretty good sustainable yields. We suggest our rule of thumb can be used to prevent data‐poor fisheries declining while capacity for more complex forms of assessment and management are developed.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it