Length-weight and length-length relationships of 16 marine fish species in Vietnam
Bibliographic record
Abstract
In this study, the length-weight relationships (LWRs) and length-length relationships (LLRs) of 16 marine fish species-important component of fishery production models-were estimated. The specimens were collected monthly from commercial gillnet fisheries from November 6, 2018 to October 30, 2019 in Vietnamese waters. In total, 7,426 individuals had their total length (TL), fork length (FL) and total body weight (W) measured. LWRs were calculated using the logarithmic transformation of the linear regression equation logW = loga + b*logTL, while LLRs were determined using a linear regression model: TL = a + b*FL. In addition, 95% confidence intervals (CIs) were estimated for the model parameters. The results showed that all regression parameters were highly significant (p < 0.001), with coefficients of determinations (R 2 ) > 0.9412 for all species. The a (intercept) values ranged from 0.0025 to 0.4, and the b (slope) values ranged from 2.53 to 3.28. TL and FL were highly correlated (p < 0.001 and R 2 > 0.9422 for all parameters), and a and b ranged from -9.12 to 15.85 and 0.87 to 1.45, respectively. The results provide the key morphology parameters, which are beneficial for fishery researchers and managers in stock assessment, administration and conservation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".