A global expert elicitation on present-day human–fire interactions
Bibliographic record
Abstract
Human fire use contributes to fire regimes and benefits societies worldwide yet is poorly understood at the global scale. We present the Global Fire Use Survey (GFUS), an effort to elicit and systematize knowledge about fire use from experts, including academics and practitioners. The GFUS data cover the stakeholders using fire, reasons for and seasonality of burning, recent trends in anthropogenic ignitions and burned area and the presence/absence and effectiveness of different policy interventions targeting fire use. The survey garnered 311 responses for regions covering over 50% of the Earth's ice-free land, improving on the coverage of literature syntheses on fire use. Here, we analyse the data on the distribution of fire use and policy interventions. The survey suggests that the most widespread fire users are Indigenous and local people burning to meet objectives associated with small-scale livelihoods and cultural priorities, whereas burning by commercial land users, state agencies and non-governmental organizations is less widespread. Regulatory restrictions are the most common policy interventions targeting fire use but are ineffective in achieving their aims in regions with higher burned area. While community-led governance of burning is rarer, it was deemed more effective than restrictive policy interventions, particularly in regions with higher burned area.This article is part of the theme issue 'Novel fire regimes under climate changes and human influences: impacts, ecosystem responses and feedbacks'.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".