OGC DISASTER RESILIENCE PILOT IV: D-123 GENERATIVE AI IN WILDLAND FIRE MANAGEMENT ENGINEERING REPORT
Bibliographic record
Abstract
As part of OGC Disasters Resilience Pilot IV (D123 — AI Advances of SDIs Report Section), this D-123 Generative AI in Wildland Fire (WF) Management Engineering Report focus was to assess the domain adaptation needed for Generative AI to support advancements in the wildland fire community as informed by user needs assessments as well as technical assessments of potential advances and efficiencies. The Wildland Fire community leverages data and advanced tools to enhance planning and operational decision-making, aiming to supplement rather than replace experiential knowledge and anecdotal evidence. GenAI, while early in its technical evolution, with a human-in-the-loop approach, offers transformative potential by enabling scalability in data processing beyond human capabilities. Exploring GenAI further, particularly in scenarios where significant efficiency gains are feasible, is imperative. Integrating GenAI into wildfire management necessitates a comprehensive strategy encompassing data, process, technical, and ethical considerations. This report explores the needed input, tools, challenges, and recommendations for next steps to implementing GenAI to support the Wildland Fire Management lifecycle. This effort had a follow-on Phase 2 (D-030) with a specific focus on Wildland Fire Insurance in Canada [OGC DISASTER RESILIENCE PILOT IV: D-030 GENERATIVE AI FOR WILDFIRE STATE-OF-THE-ART ENGINEERING REPORT].
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".