Bibliographic record
Abstract
The 2018 stock assessment modelling framework review for 4X5Y Cod was held on November 6–8, 2018. It was the second of two Maritimes Region of Fisheries and Oceans Canada peer-review meetings for 4X5Y Cod. This meeting was preceded by the Assessment Framework for 4X5Y Atlantic Cod: Part 1 – Review of Data Inputs, held on March 27–28, 2018. This research document summarized the conclusions from the ‘data inputs’ meeting and described the methodologies developed for estimating current stock status, reference points, and short term projection for providing muti-year catch advice. A 10-year, medium-term projection was conducted to evaluate the recovery potential. Guidance on inter-framework review activities, existing data gaps, and research recommendations in the future were documented. Both a Virtual Population Analysis (VPA) model and State-space Assessment Model (SAM), with assumed constant natural mortality (M), were used to demonstrate the fitting problems caused by the unexplained high mortality reflected in the survey and fishery data. To resolve the retrospective pattern and other model fitting issues observed in past 4X5Y Cod stock assessments, the VPA models with random walk in M by different age groups were explored. A VPA model (3MFfirst) was recommended as the new framework model for the 4X5Y Cod stock assessment. There was a clear temporal increasing trend in M on older age groups (ages 5+); it has steadily increased from 0.37 in 1983 to about 1.5 since 2011. The high M estimated in the VPA models could be aliasing fish moving to adjacent areas or deeper waters, under-reported landings, and unreported discards, as well as natural mortality caused by biotic and abiotic factors. For the biomass limit reference points, considering the unstationary natural mortality of ages 5+ Cod, the equilibrium Maximum Sustainble Yield (MSY)-based reference points was considered not approporiate. The Sb50/90 = 22,193 mt was recommended as the biomass limit reference points for 4X5Y Cod. Given the estimated poor stock status, the fishery catch advice would be as low as possible to rebuild this stock. The fishing reference point was not developed. A 10-year projection showed that there would be little chance of stock recovery under high M and low recruitement, even with zero catch.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".