Assessments of 14 Exploited Fish and Invertebrate Stocks in Chinese Waters Using the LBB Method
Bibliographic record
Abstract
Due to lLimited by data availability, only a small subset of the exploited fish and invertebrate populations have been assessed along Chinese coastsal waters, which precludes comprehensive management of the fisheries. Here, we applied a length-based Bayesian biomass estimator (LBB) to 14 fish and invertebrate stocks in China’s coastal waters to estimate their growth, length at first capture and current relative biomass (B/B0, B/BMSY) from length-frequency (LF) data. Of the 14 populations assessed, 1 have collapsed, 9 are grossly over-exploited, and 3 are overfished. Moreover, 13 populations have smaller mean lengths at first capture (Lc) than the optimal length at first capture (Lc_opt), indicating that they are suffering from growth overfishing. Thus, larger mesh sizes in commercial fishery would increase both the catch and biomass for these species, given current levels of fishing mortality. Our results confirm that fishery resources in China’s coastal waters are strongly depleted, and that stricter management measures are needed to restore the abundance of China’s marine fisheries resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".