A prospective framework to support climate-adaptive fisheries in Canada
Bibliographic record
Abstract
Climate change affects virtually all marine life and is increasingly a dominant concern for fisheries, reinforcing the need to incorporate climate variability and change when managing fish stocks. Canada is expected to experience widespread climate-driven impacts on its fisheries but does not yet have a clear adaptation strategy. Here, we provide an overview of a project we are developing, the Climate Adaptation Framework for Fisheries, to address this need and support climate adaptation in Canadian marine fisheries. The framework seeks to quantitatively and flexibly evaluate species, fishing infrastructure, and the management and operation of fisheries to assess climate vulnerability comprehensively and provide outputs that can support climate adaptation planning across different sectors, agencies, and stakeholders. This new framework should allow future climate scenarios to be evaluated and identify actionable climate vulnerabilities related to the management of fisheries, creating a systematic approach to supporting climate adaptation in Canada’s fisheries.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".