Assessment of Tropical Fish Stocks Using the LBB Method in Dongzhaigang Bay, Hainan Island, China
Bibliographic record
Abstract
This study used catch/effort (CPUE) and length-frequency (L/F) data to evaluate the status of 26 fish species in the mangroves of the Dongzhaigang National Reserve, Hainan Province, China, sampled in 2009 (16 species), 2014 (18 species) and 2020 (15 species) using CPUE and the length-based Bayesian biomass (LBB) method. The CPUE, both in number and weight, was lower in 2009 than in 2014 and lower again in 2020, with the 2009 low attributed to pollution due to shrimp, pig and duck farming within the reserve. Of the 26 fish species assessed, four experienced a large reduction of length from 2009 to 2020, and nine exhibited a decline in the ratio of current biomass to biomass at carrying capacity (B/B0), which is expressed as the ‘biomass left’. This ratio was, for most species, below 0.5 in 2009, which suggests that overfishing occurred in 2009 and that it has since become worse. Thus, while the CPUE data provided ambiguous results, the L/F data analyzed by the LBB method demonstrated unambiguously that ‘miniaturization’ through overfishing is occurring among the exploited 26 fish species from the mangroves of the Dongzhaigang National Reserve. For the fisheries in the mangals of Dongzhaigang Bay to remain viable, fishing effort should be reduced by local governments working with the affected communities, just as they reduced pollution a decade ago.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.000 | 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".