OGC DISASTER RESILIENCE PILOT IV: D-030 GENERATIVE AI FOR WILDFIRE STATE-OF-THE-ART ENGINEERING REPORT
Bibliographic record
Abstract
As part of OGC Disasters Resilience Pilot IV Phase 2, this D-030 Generative AI for WildFire State-of-the-Art Engineering report delivers a comprehensive Generative AI for Wildfire which assessed Generative AI technology on workflows for wildfire risk, hazard, and impact workflows typical in the insurance sector. This deliverable builds on Xentity’s expertise and contributions to Phase 1 (D-123) for advancing the integration of Generative AI (GenAI) technologies into wildfire risk, hazard, and insurance workflows. Phase 1 (D-123) provided a U.S. data focus across all wildfire use cases and went deeper into broader GenAI governance, capabilities and technology approaches in LLM, RAG, NLP integration, GANs, and AI Agent integration. This report outlines key GenAI-driven use cases relevant to wildfire resilience, response, and risk assessment. This report centers on leveraging Generative AI (GenAI) to strengthen wildfire insurance and preparedness efforts in Canada, addressing social impact, operational efficiency, and business resilience. Specifically, the use case focus, and needed data focuses on Helping People and Business Management as it relates to Wildland Fire Insurance Stakeholders. Phase 2 includes an inventory of over 200 Canadian wildfire-related data sources categorized in data subject areas of Wildland Fire National Strategy & Management, National Base Data Layer Information, and Risk Indicators, Analysis, and Assessment which would be needed for GenAI Training data.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".