Incorporating knowledge of changes in climatic, oceanographic and ecological conditions in Canadian stock assessments
Bibliographic record
Abstract
Abstract Environmental impacts on fisheries are pervasive, yet methods to account for them in stock assessments and management decisions vary in rigour and quality. The prevalence and efficacy of methods to account for environmental impacts are not well documented, limiting our ability to adequately respond to future environmental and climate changes for adaptive resource management. In Canada, legislation now requires that environmental conditions are considered in the management of fish stocks, yet the current extent of implementation in assessment processes is poorly understood. We assessed the use of climate, oceanographic and ecological considerations in science advisory processes for 178 stock assessments by Fisheries and Oceans Canada. We evaluated whether these considerations were included in conceptual hypotheses about broad‐scale mechanisms, quantitative or qualitative analyses, and the development of management advice on current or future stock status. Conceptual hypotheses were included in 46% of assessments; quantitative inclusions occurred in 21% of assessments, while qualitative interpretations appeared in 31% of assessments; and 27% of assessments included climate, oceanographic and/or ecological considerations in the advice. Assessments of salmonids, invertebrates and pelagic taxa more frequently made use of environmental data than those for groundfish and elasmobranchs. Comparing our findings with assessments in other jurisdictions highlighted a gap in Canada's ability to respond to environmental changes and a need to develop integrated management approaches, such as regional ecosystem assessments and approaches that combine modelling and empirical analyses, with socio‐economic analysis within interdisciplinary teams.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".