A quantitative evaluation of the pollock <i>Gadus chalcogrammus</i> restocking programme in South Korea
Bibliographic record
Abstract
The collapse of the Korean pollock ( Gadus chalcogrammus) stock in the late 1990s is hypothesised to be due to environmental changes, overexploitation, or a combination of both factors. However, due to limited data availability, no conclusive scientific analysis is available to support any of these hypotheses. In response to the stock collapse, the South Korean government initiated a restocking programme in 2015. As of early 2024, approximately 1.9 million young-of-the-year pollock have been released into the ocean. Despite nearly a decade of implementing this programme, there has been no indication of stock recovery. This study quantitatively evaluated the potential for stock rebuilding under varying restocking rates to assess the efficacy of the restocking programme. Given the scarcity of historical data on the stock, we applied a stochastic stock reduction analysis using a length-based, age-structured model developed specifically for this analysis. A wide range of alternative models was considered to account for structural and parameter uncertainties. The results suggest that, even under the most optimistic assumptions, the restocking practice is unlikely to facilitate stock rebuilding.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".