Fishing and Natural Mortality Rates of Atlantic Halibut Estimated from Multiyear Tagging and Life History
Bibliographic record
Abstract
Abstract Fishing mortality and natural mortality rates for Atlantic HalibutHippoglossus hippoglossuson the Scotian Shelf and southern Grand Banks were estimated from a multiyear tagging study. Models that were used to estimate mortality rates incorporated tag loss. Between 2006 and 2008, 1,913 Atlantic Halibut were double‐tagged with t‐bar anchor tags; as of 26 August 2010, 368 of these fish had been recaptured. We estimated instantaneous fishing mortality (F) separately for each cohort in the first year (on average, 6 months) after release to allow newly tagged animals to mix with the population. A two‐parameter model was used to describe tag loss. Tag loss was estimated at 13% per year in the first year and 10% per year in the second and subsequent years. Using the multiyear model with incomplete mixing and assuming 90% tag reporting and 80% survival from tagging, average instantaneous natural mortality (M) of Atlantic Halibut was estimated to be 0.22 andFwas estimated to be 0.15 in 2007, 0.24 in 2008, and 0.18 in 2009. These estimates ofFwere comparable to those from the stock assessment population model. However, the estimates ofMwere higher than inferred estimates ofMbased on life history and growth. Estimates ofFandMwere sensitive to the minimum size of Atlantic Halibut at the time of release. An increase inFwith size is consistent with fishery size selectivity resulting from either gear selectivity or the distribution of fishing effort where there is spatial heterogeneity in the size composition. We suggest thatMmay have been overestimated because of emigration from the study area.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".