Climate Crisis and Wildfire: A Call for Environmental Assessment and Policy Formulation Prioritizing Indigenous Knowledge
Bibliographic record
Abstract
Canada has a longstanding history of wildfires caused by natural and human factors across provinces. While continuous monitoring has reduced human-triggered fires, the overall wildfire counts increases yearly. The 2023 wildfire was notably destructive, setting records for its scale, duration, and impact. This trend worsens due to rising temperatures, dry conditions, reduced vegetation moisture, droughts, and climate change. Canada's Environmental Impact Assessment (EIA) recognizes climate change indicators but lacks clear guidance on addressing wildfires, notably in fire-prone regions. Indigenous communities bear the brunt of wildfires and possess invaluable forest management knowledge that must be integrated into contemporary strategies before they are lost. Proper collaboration with indigenous people and concerned efforts to mitigate climate change offer hope in reducing future wildfire devastation and alleviate the environmental and societal impacts of wildfires.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".