Scientists’ warning on wildfire — a Canadian perspective
Bibliographic record
Abstract
Recently, the World Scientists’ Warning to Humanity: a Second Notice was issued in response to ongoing and largely unabated environmental degradation due to anthropogenic activities. In the warning, humanity is urged to practice more environmentally sustainable alternatives to business as usual to avoid potentially catastrophic outcomes. Following the success of their warning, the Alliance of World Scientists called for discipline-specific follow-up papers. This paper is an answer to that call for the topic of wildland fire. Across much of Canada and the world, wildfires are anticipated to increase in severity and frequency in response to anthropogenic activities. The world scientists’ second warning provides the opportunity for wildland fire researchers to raise the profile of the potential impacts that anthropogenic activities are likely to have on future fire regimes and, in return, what impacts future fire regimes may have on humanity. We discuss how wildfire is related to several issues of concern raised in the world scientists’ second warning, including climate change, human population growth, biodiversity and forests, and freshwater availability. Furthermore, we touch on the potential future health impacts and challenges to wildfire suppression and management in Canada. In essence, our wildfire scientists’ warning to humanity is that we, as a society, will have to learn to live with more fire on the landscape. We provide some recommendations on how we might move forward to prepare for and adapt to future wildfire regimes in Canada. Although this paper is primarily Canadian in focus, the concepts and information herein also draw from international examples and are of relevance globally.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 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".