Assessments of 15 Exploited Fish Stocks in Chinese, South Korean and Japanese Waters Using the CMSY and BSM Methods
Bibliographic record
Abstract
The status of fifteen marine fish and invertebrate populations exploited by Chinese, South Korean or Japanese fishing fleets were assessed, using two newly developed computer-intensive methods, CMSY and BSM. The results show that among these fifteen populations in question, 2 have collapsed, 3 are grossly over-fished and 9 are overfished. Also, we compared results of the CMSY and BSM methods, and it shows that, while the CPUE data for use with the BSM method lead to narrower confidence intervals and may modify the shape of the biomass trajectory, they do not lead to over- or underestimates of terminal B/BMSY values. These results, although still tentative because the methods used to generate them are relatively new, generally match what is known of the status and exploitation of the populations in question, which makes CMSY and BSM to be promising stock assessment approaches in data-sparse situations. Based on our results, a consistent signal is obtained, thus suggesting that a reduction in fishing pressure would be necessary to restore the abundance status of these fifteen populations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".