Bayesian state-space modelling of the De Lury depletion model: strengths and limitations of the method, and application to the Moroccan octopus fishery
Bibliographic record
Abstract
Abstract Robert, M., Faraj, A., McAllister, M. K., and Rivot, E. 2010. Bayesian state-space modelling of the De Lury depletion model: strengths and limitations of the method, and application to the Moroccan octopus fishery. – ICES Journal of Marine Science, 67: 1272–1290. The strengths and limitations of a Bayesian state-space modelling framework are investigated for a De Lury depletion model that accommodates two recruitment pulses per year. The framework was applied to the Moroccan fishery for common octopus (Octopus vulgaris) between 1982 and 2002. To allow identifiability, natural mortality (M) and the recruitment rhythm were fixed, and the variance of both process and observation errors were assumed to be equal. A simulation–estimation (SE) approach was derived to test the performance of the method. If the data showed responses to harvest, the estimates of the most important figures, i.e. the initial abundance and the second recruitment pulse, were accurate, with relatively small bias. Results confirm that greater depletion yields smaller bias and uncertainty and that inferences are sensitive to the mis-specification of M. The 21 depletion series in the Moroccan dataset were jointly treated in a hierarchical model including random walk to capture the systematic fluctuations in estimates of catchability and initial abundance. The model provides estimates of the annual recruitment and monthly octopus population size. The recruitment estimates could be used to investigate the link between recruitment variability and the coastal North African upwelling regime to improve understanding of the dynamics and management of octopus stocks.
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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 | 0.000 |
| 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) | 0.000 | 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".