Aligning monitoring design with fishery decision-making: examples of management strategy evaluation for reef-associated 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
Limitations in data quantity and quality are common in fisheries management and affect whether and how stock assessments are carried out. We demonstrate the applicability of spatially-explicit management strategy evaluation (MSE) for connecting sampling designs of substrate-associated fishes in highly heterogeneous habitats to evaluation of stock status and to decision-making. Simulation-based analysis is conducted to understand how survey precision influences assessment and decision-making performance for a black grouper (Mycteroperca bonaci) fishery within the Florida Keys reef tract. Rather than delving into statistical principles of survey design, we examine the performance of well-designed surveys in the currency of achieving fishery management objectives. In practical terms, our approach draws attention to subtle aspects of how data precision can affect achievement of management objectives. Our discussion centers on the idea that the interconnected properties of survey precision, stock assessment, and precaution taken in decision-making must be jointly considered in fisheries management policies.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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 it