Estimating exploitation rates of migrating yellowtail flounder (Limanda ferruginea) using multistate mark–recapture methods incorporating tag loss and variable reporting rates
Bibliographic record
Abstract
Multistate mark–recapture models can be used to model migration through stratification of the study area into states (location). However, the incorporation of both tag loss and reporting rates is new to the multistate paradigm. We develop a migration model for fish that incorporates tag loss and reporting rates but has as its primary purpose the modelling of exploitation and natural mortality rates. This model is applied to a 2000–2004 yellowtail flounder ( Limanda ferruginea ) tagging study on the Grand Bank of Newfoundland, Canada. We found that exploitation rates varied over both location and years, ranging from 0.000 to 0.047. Migration into the centre of the Grand Bank (state 2) was three times higher than migration out. The estimate of the instantaneous annual natural mortality rate was 0.256, which is equivalent to an annual survival rate of 0.880. We describe how these mortality estimates will be quite valuable in specifying an assessment model for this stock.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".