Transfer of climate knowledge via a regional climate-change management body to support vulnerability, impact assessments and adaptation measures
Bibliographic record
Abstract
We provide an overview of how climate-change-science knowledge transfer is achieved at Ouranos in support of vulnerability and impact assessments and adaptation (V&I&A) activities. Ouranos is a Canadian consortium concerned with regional climatology and adaptation to climate change, launched in 2002 by the Government of Qubec, Hydro-Qubec and the Meteorological Service of Canada to coordinate climate-change research in Qubec. Focusing on Ouranos' ongoing V&I&A projects in coastal regions, we describe the current knowledge-transfer environment. We also discuss how climate indices and indicators of vulnerability -which are developed by Ouranos following a 'pressure-state-response' (PSR) framework -form useful knowledge-transfer tools. Two specific case studies exemplify the development of (1) a set of temperature-trend indices for southern Qubec and (2) climate-social indicators for the assessment of risks to public health due to extremely high temperature events. Case Study (1) illustrates how a systematic analysis of climate variability and relevant indices of extremes can be useful for decision makers at regional scales (southern Qubec). Case Study (2) examines the potential, and feasibility, of using a risk-assessment framework for regional climate-change studies that focus on impacts and adaptation.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".