Rethinking resilience to wildfire
Bibliographic record
Abstract
Record-breaking fire seasons are becoming increasingly common worldwide, and large wildfires are having extraordinary impacts on people and property, despite years of investments to support social–ecological resilience to wildfires. This has prompted new calls for land management and policy reforms as current land and fire management approaches have been unable to effectively respond to the rapid changes in climate and development patterns that strongly control fire behaviour and continue to exacerbate the risks and hazards to human communities. Promoting social–ecological resilience in rapidly changing, fire-susceptible landscapes requires adoption of multiple perspectives of resilience, extending beyond ‘basic resilience’ (or bouncing back to a similar state) to include ‘adaptive resilience’ and ‘transformative resilience’, which require substantial and explicit changes to social–ecological systems. Clarifying these different perspectives and identifying where they will be most effective helps prioritize efforts to better coexist with wildfire in an increasingly flammable world. Record-breaking fire seasons are becoming the new normal, prompting calls for land management and policy reforms. This Perspective clarifies different types of resilience to wildfire to prioritize efforts to better coexist with increasingly fire-prone conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".