Igniting Progress: Outcomes from the FLARE workshop and three challenges for the future of transdisciplinary fire science
Bibliographic record
Abstract
Fire substantially influences and modulates the global carbon cycle through numerous processes, interactions, and feedbacks. Fires are strongly intertwined with human activities: people act both as drivers of change through ignitions, suppression, land-cover change, prescribed burning, and climate change, and are affected in return by changes in fire regimes. Despite fire’s many complex interactions throughout the Earth System, it is often viewed only as a destructive process, and one that solely acts as a source of atmospheric carbon. In terms of fire’s carbon budget, the release of carbon only represents the very initial stages of the process, missing the drivers and complex ways in which fire shapes plant species evolution and ecosystem trajectories, nutrient cycling and redistribution, carbon allocation, deposition and sequestration over different spatiotemporal scales. Therefore, there is a clear need to fully understand the role of fire in the Earth System holistically. However, different aspects of fire’s role in the carbon cycle are often studied by different communities and disciplines, hindering this much-needed integrated understanding. Through the Fire Learning AcRoss the Earth Systems (FLARE) workshop (September 2023) we brought together fire scientists across multiple disciplines to facilitate transdisciplinary discussion. We highlight a poor constraint on understanding the future impacts of fires on the Earth system, which stems from a lack of communication between relevant fields of expertise. In particular, a disconnect was identified between scientific approaches to understanding and characterising fire processes and the societal implications of fire events. We identify three main challenges that need to be addressed by the global fire community: unifying transdisciplinary research around common boundary objects, further understanding and quantifying the role of fire in the carbon cycle; better characterising fire and extreme events; and taking a holistic approach to understand fire interactions with humans.
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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 | 0.038 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".