The 2023 wildfires in British Columbia, Canada: impacts, drivers, and transformations to coexist with wildfire
Bibliographic record
Abstract
In 2023, all regions of British Columbia (BC) experienced record-breaking fire weather and wildfires, with extreme behavior and social-ecological effects. In total, 2245 wildfires burned 2840 545 hectares. Contemporary wildfires are the culmination of a century of altered human–forest–wildfire relationships, exacerbated by climate change. Transformative change is urgently needed for the ecosystems and communities to be resilient to wildfire. We present six interrelated strategies needed to amplify the pace and scale of change in response to recent wildfire extremes: (1) Immediately diversify wildfire response strategies and restore the ecological and cultural role of fire in BC’s ecosystems. (2) Invest in suppression capacity at local and national scales. (3) Support innovations to overcome the economic barriers for mitigating risk and building resilience within communities and the wildland-urban interface. (4) Apply landscape fire management to drive a paradigm shift in forest management to increase ecological resilience to wildfire. (5) Transform wildfire governance to support collaborative and community-based solutions. (6) Strengthen expertise and capacity to uplift diverse ways of knowing, managing, and coexisting with fire. These strategies, combined with bold policy and governance changes and supported by sustained funding programs, provide a holistic approach to transform management and coexist with wildfire.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".