Stock status and management procedure performance for the BC Sablefish (Anoplopoma fimbria) fishery for 2022/23
Bibliographic record
Abstract
The British Columbia Sablefsh fshery has been managed using a simulation tested harvest strategy since 2011. The operating model used to generate simulated stock and fshery data is revised at intervals to refect new data and hypotheses about the management system. We use this paper to detail the migration of the Sablefsh operating model (SAB-OM) from the unsupported AD model builder (ADMB) language to the leading-edge Template Model Builder (TMB) language. We detail a rigorous transition and bridging analysis between 2018 and 2021 data and hypotheses, as well as testing against several sensitivities, including at-sea release model assumptions, age composition likelihood weightings, and leading parameter prior distributions. At the same time, we also transition the Schaefer production model used to estimate biomass and operational control points within the Sablefsh management procedure (MP) from ADMB to TMB, and re-evaluate performance of the updated MP against operational fshery objectives. Sablefsh biomass, productivity, and stock status for 2022, estimated over an ensemble of fve SAB-OM ‘reference set’ hypotheses, indicate that the stock is currently not overfshed and not experiencing overfshing. The spawning biomass at the start of 2022 is approximately 132% of optimal biomass BMSY , and the 2021 harvest rate is about 71% of UMSY , with recent dynamics driven by three large incoming year classes. Estimates of recruitment deviations from the base SAB-OM hypothesis are compared to environmental indices, but no signifcant relationships are evident, indicating that a more in-depth research project may be required to determine if a link exists that would have implications for operating model formulation or management procedure design. Finally, all tested Sablefsh MPs, including the status-quo MP with a 5.5% harvest rate, meet the biomass conservation and target objectives across the reference set of operating models, satisfying Canada’s precautionary approach harvest policy requirements for management by reference points.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".