Stock Assessment for Seven Fish Species Using the LBB Method from the Northeastern Tip of the Bay of Bengal, Bangladesh
Bibliographic record
Abstract
Assessment of fish stock status is generally required for fisheries management, which is difficult when the data are limited. The length-based Bayesian Biomass (LBB) approach is a powerful and new method, where only the length-frequency data are used for estimating the status of fisheries resources. Here, we applied the LBB method to assess the status of seven commercially valuable marine fishes from the northern tip of the Bay of Bengal (BoB), Bangladesh. These species were Lepturacanthus savala, Pampus argenteus, Nemipterus japonicas, Nemipterus randalli, Ilisha filigera, Saurida tumbil, and Upeneus sulphurous. The current relative biomass (B/B0) ratios were smaller than the BMSY/B0 in five stocks, except for N. japonicas and N. randalli, and this indicates that, of the seven populations assessed, two are grossly overfished, three are overfished, and two are healthy stocks. Moreover, the length at first capture (Lc) was lower than the optimal length at first capture (Lc_opt) in all seven populations, which indicates growth overfishing, suggesting that increasing the mesh sizes would be beneficial. The present findings confirm that Bangladesh’s coastal water fishery resources are declining. More specific targeted management measures should be taken to recover the country’s marine fishery resources.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".