AGU24 - GC11K-0078A: A comprehensive network analysis of people working in fire monitoring in Canada
Bibliographic record
Abstract
There is a growing call to increase collaboration, inclusion, and engagement with end-users in applications such as remote sensing, Earth observation, artificial intelligence, and fire sciences. In response, Canada is making significant investments in cultivating collaboration and user engagement in fire and fire monitoring. Initiatives include the Canadian Forest Service’s Wildfire Resilient Futures Initiative and the upcoming WildFireSat mission. The fire monitoring community relies heavily on geospatial technologies, Earth observation, and remote sensing, benefiting greatly from researchers with diverse expertise and experiences. To advance inclusion and collaboration in fire monitoring, it is important to understand who is working in the field, their networks, and perceived barriers to research and collaboration. This presentation will overview the multi-year “Underserved Actors in Canadian Fire Monitoring” program and its early findings. The program is divided into three phases: 1) a bibliometric and initial network analysis to identify fire monitoring actors in Canada, 2) a survey of fire monitoring actors and enhanced social network analysis using identity-based information, 3) interviews with key actors to identify opportunities for improving collaboration and inclusion in fire monitoring. We will present outcomes from Phase 1, including an overview of the Canadian fire monitoring actors database and the results from a spatial social network analysis. Data on Canadian fire monitoring actors was collected using a systematic search of publication databases in combination with purposive sampling of research consortiums and fire organizations. Our results characterize the types and levels of connectivity within the Canadian fire monitoring community, which will guide subsequent survey and interview questions for the second phase of the study. This research has contributed to broader international efforts to characterize global fire monitoring users as part of the Committee on Earth Observation Satellite Working Group on Disasters Wildfire Pilot program. Additionally, outcomes from this research can be used to tailor resources from future collaboration and capacity-building efforts in fire monitoring.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.045 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".