Advancing Wildfire Research Through Big Data, Artificial Intelligence and Emerging Image Processing Algorithms
Bibliographic record
Abstract
More and larger wildfires in northern Canada are causing environmental damage, releasing stored carbon, and forcing residents to relocate. With climate change factors such as warmer winters and broader spread of damaging insects, Yukon is experiencing earlier, longer, and more intense wildfire seasons. This paper furthers our understanding of wildfire severity mapping by: (1) assessing advancements in remote sensing data, cloud geoprocessing platforms and emerging image processing algorithms for wildfire burned area mapping, (2) conducting a driver analysis of wildfire severity in Yukon using the LandTrendR temporal segmentation algorithm, the Google Earth Engine cloud geoprocessing platform, and a deep neural network, and (3) analyzing the influences that a diverse series of climate, topographic, ecological and fire history variables had on wildfire severity. These new techniques and tools, enhancing the accuracy of burn severity models, could improve Canadian and global forest management practices, potentially reducing the impact of severe wildfires on humans.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".