Integrated fire management as an adaptation and mitigation strategy to altered fire regimes
Bibliographic record
Abstract
Altered fire regimes are a global challenge, increasingly exacerbated by climate change, which modifies fire weather and prolongs fire seasons. These changing conditions heighten the vulnerability of ecosystems and human populations to the impacts of wildfires on the environment, society, and the economy. The rapid pace of these changes exposes significant gaps in knowledge, tools, technology, and governance structures needed to adopt informed, holistic approaches to fire management that address both current and future challenges. Integrated Fire Management is an approach that combines fire prevention, response, and recovery while integrating ecological, socio-economic, and cultural factors into management strategies. However, Integrated Fire Management remains highly context-dependent, encompassing a wide array of fire management practices with varying degrees of ecological and societal integration. This review explores Integrated Fire Management as both an adaptation and mitigation strategy for altered fire regimes. It provides an overview of the progress and challenges associated with implementing Integrated Fire Management across different regions worldwide. The review also proposes five core objectives and outlines a roadmap of incremental steps for advancing Integrated Fire Management as a strategy to adapt to ongoing and future changes in fire regimes, thereby maximizing its potential to benefit both people and nature.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".