DFO climate change science community needs evaluation 2025
Bibliographic record
Abstract
Demand is increasing for science advice to support the inclusion of climate change considerations in decision making at Fisheries and Oceans Canada (DFO). In response, DFO’s Climate Change Science community gathered on March 19, 2025, to discuss science and cross-sectoral needs related to departmental climate change risks stemming from changes such as shifting species distributions, ecosystem degradation, and sea level rise. The needs identified were further discussed and refined in subsequent discussions. Key needs identified include stabilizing, strengthening, and increasing efficiency of observations and documenting climate change trajectories, extremes, and impacts; advancing high-resolution climate models; advancing research to support the understanding of climate change impacts; developing species distribution models, ecosystem models, and interdisciplinary assessments (vulnerability, risk, and cumulative impacts); synthesizing existing tools; and translating science into accessible, and easy-to-use formats for decision makers. While the majority of needs (n = 56) fall within the purview of the Ecosystems and Oceans Science Sector, 43 (including strengthening the science-policy-management interface and leverage partnerships to tackle cross-cutting climate issues) necessitate cross-sectoral (n = 29) or interdepartmental (n = 14) collaboration. This report provides a foundation for strategizing and prioritizing science activities and their mobilization into decision making to build a more climate-resilient DFO, allowing better support for Canadian aquatic ecosystems, species, communities, infrastructure, and industries through ongoing environmental change.
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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 | 0.046 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.053 | 0.009 |
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".