Application of the PROCEAN model to the eastern Atlantic skipjack (#Katsuwonus pelamis$) tuna fishery
Bibliographic record
Abstract
The PROCEAN (PRoduction Catch / Effort ANalysis) model is a multi-fleet biomass dynamic model developed in a Bayesian framework to conduct stock assessments based on catch and effort time series data. PROCEAN was applied to the eastern component of the Atlantic skipjack (Katsuwonus pelamis) to assess the current status of the stock and provide reference points to fishery managers. Data included 8 time series of catch and effort data spanning from 1969 to 2006 and mostly standardised based on generalized linear modelling techniques. Informative priors and constraints for some parameters were used to allow the model to converge. Model fits were generally good but indicated increasing trends in the residuals for some fishing fleets. Results suggested that the eastern Atlantic skipjack stock would be underexploited, with a biomass in 2006 equal to 1.8 times the biomass at the maximum sustainable yield (MSY) and a fishing mortality in 2006 equal to 30% of the fishing mortality at MSY (FMSY). A sensitivity analysis performed to account for uncertainty in some input parameters showed that FMSY estimates were robust to parameter changes while MSY estimates were sensitive to the prior distribution for MSY. Based on the different model runs, the MSY was estimated to be comprised between 152,000 and 185,000 t. The use of random walks on catchability was useful for tracking changes in catchability and associated increasing fishing power for some baitboat fishing fleets targeting skipjack.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".