A sequential Bayesian methodology to estimate movement and exploitation rates using electronic and conventional tag data: application to Atlantic bluefin tuna (Thunnus thynnus)
Bibliographic record
Abstract
This paper presents a Bayesian methodology to estimate fishing mortality rates and transoceanic migration rates of highly migratory pelagic fishes that integrates multiple sources of tagging data and auxiliary information from prior knowledge. Exploitation rates and movement rates for Atlantic bluefin tuna ( Thunnus thynnus ) are estimated by fitting a spatially structured model to three types of data obtained from pop-up satellite, archival, and conventional tags for the period 1990–2006 in the western North Atlantic. A sequential Bayesian statistical approach is applied in which the key components of the model are separated and fitted sequentially to data sets pertinent to each component with the posterior probability density function (pdf) of parameters from one analysis serving as the prior pdf for the next. The approach sequentially updates the estimates of age-specific fishing mortality rates (F) and transoceanic movement rates (T). Estimates of recent F are higher than the estimated rate of natural mortality and higher in the east than in the west. Estimates of annual T from the west to the east are higher for larger fish (6% for ages 0–3 to 16% for ages 9+). These estimates are also higher than those obtained from tagging studies before the 1990s and could be associated with changes in stock composition.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".