Burning issues: statistical analyses of global fire data to inform assessments of environmental change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Global pyrogeographic study is necessary to inform climate change impact assessments used for management and decision‐making. Climate is a strong driver of spatial and temporal patterns of fire such that ongoing climate change is expected to alter global fire activity. A growing number of statistical–correlative analyses examine environmental drivers of current patterns of global fire occurrence or burned area, but few studies ask important “what if” questions about the potential future of fire under scenarios of a changing climate. Accordingly, our goal is to engage the broader statistical community in analysis of global fire data products to spur further understanding of fire regimes and the complex links they demonstrate between the biosphere and atmosphere. We provide an overview of constraints over fire regimes and the role of fire in the biosphere–atmosphere, describe general approaches being used for global fire–climate assessment, summarize opportunities and pitfalls in the public‐access global fire datasets, and highlight thinking on next steps for analysis of global fire and fire regime data. Copyright © 2014 John Wiley & Sons, Ltd.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it