BAYESIAN LENGTH ANALYSIS AND EXPLOITATION RATES FROM THE MAIN TARGET SHARK SPECIES CAUGHT IN THE NORTHWESTERN MEXICAN PACIFIC: A PREAMBLE TO FISHERY INDICATORS
Bibliographic record
Abstract
Sharks in Mexico have economic, fishing, and social importance; however, there are no complete assessments of their populations, mainly due to scarce and inadequate catch and effort data. Nevertheless, through size frequency analysis, it is possible to obtain preliminary fishing indicators to know the status of an exploited population. This study analyzes fishing- dependent data (sizes and sexes) of nine species of pelagic sharks from data collected onboard medium-size shark vessels in the Mexican Pacific from 2006 to 2018. Our results suggest that the average lengths in the catch have remained constant throughout the study period. Similarly, exploitation rates remained below the benchmarks proposed by the literature. However, the results presented in this study should be taken cautiously and only as a preliminary analysis until more complete studies are carried out. Keywords: bayesian approach, Mexican Pacific, pelagic sharks, total mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".