Graphical Evaluation of Fishery Status Using a Likelihood Inference Approach
Bibliographic record
Abstract
Abstract We present a graphical method that uses a statistical likelihood approach to summarize evidence and risk in fishery status evaluation. The graphical method is based on the surplus production model and shows the fishery status as regions and colors on graphs. Two fishery status graphs are used: one is based on the comparison between observed catch and catch at the fishing mortality level corresponding to maximum sustainable yield; the other one is based on the comparison between observed catch and catch at the surplus production level. The graphical statistical likelihood approach quantifies the strength of evidence in supporting different hypotheses of fishing status as regions in the status graphs. A simulated hypothetical fishery is given as an example. Results are graphically presented to show the exploitation status of the fishery, and they compare favorably with those from a composite risk assessment method. This graphical statistical likelihood approach may improve the communication of knowledge and evidence among scientists, fishery stakeholders, and management agencies and may provide a better understanding of current fishery status.
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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 | 0.019 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".