A Bayesian mark–recapture model for multiple-recapture data in a catch-and-release fishery
Bibliographic record
Abstract
This paper extends a state–space Bayesian mark–recapture framework to multiple-recapture data to estimate fishery-specific capture and mortality rates and seasonal movement rates for fish in different length classes. The methodology is applied to tag recapture data for white sturgeon ( Acipenser transmontanus ) collected in the recreational fishery and the Canadian Department of Fisheries and Ocean’s test fishery at Albion in the lower Fraser River. Significant differences were found between some estimated movement rates by season and length class, supporting the notion of there being marked differences in seasonal movement patterns between different life history stages of A. transmontanus in the lower Fraser River. Uncertainty in the tag reporting rate parameter, quantified using a recreational creel sampling program, is summarized by a prior distribution. The utility of recreational fishing effort as a model covariate in accounting for seasonal and spatial variation in recapture rates is addressed using Bayesian model evaluation criteria. The data provide strong support in favour of models that include fishing effort as a covariate. The appropriate level of stratification for the recreational catchability parameter q is assessed using Bayesian model evaluation criteria; models in which q is estimated by season and length class have the highest posterior probabilities.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".