Ontario Climate Risk: Workshop Report
Bibliographic record
Abstract
Despite having the country's largest economy, population, and number of universities with world-class expertise on the topic, Ontario lacks a hub for sharing information and best practices, and fostering connections between those working to address climate risk. There is a need for rigorous inquiry into localized climate impacts, including the potential for increased frequency and intensity of heatwaves, disruptions in water availability, and impacts on the Great Lakes region ecosystems. The significant expertise amongst academics and other researchers across the region regarding the complex dynamics between these factors will be necessary for devising effective and equitable mitigation and adaptation strategies. Identifying and addressing these gaps in our knowledge is paramount for developing region-specific strategies to mitigate and adapt to climate change, thereby contributing to the overall resilience and sustainability of Ontario's communities. In this context, the Ontario Climate Risk Workshop, held on October 30-31, 2024, brought together participants from academia, public and private sectors, non-governmental organizations, Indigenous leaders, elected officials, and representatives of the general public to share knowledge, discuss existing initiatives, and co-create a research agenda for addressing climate risk in the province. The event was structured around eight thematic sessions, each of which is documented in this report. Within each session, participants examined and discussed existing resources and barriers relevant to addressing climate risks associated with the respective theme. These proceedings provide an overview of the discussions for each session and were co-developed by our research team along with the respective session leads.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.010 |
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".