Representing Northern High Latitude Peat Fires in the JULES-INFERNO Fire Model
Bibliographic record
Abstract
Anthropogenic activities and climate change are increasing the vulnerability of carbon rich peatlands to wildfires. Peat fires, which are dominated by smouldering combustion, are some of the largest and most persistent wildfires on Earth. Across the northern high latitudes, peat fires have the potential to release vast amounts of long term stored carbon and other greenhouse gases and aerosols. Consequently, peat fires can have huge implications on the carbon cycle and result in a positive feedback effect on the climate system. Peat fires also impact air quality and can lead to haze events, with major impacts on human health. Despite the importance of peat fires they are currently not represented in most fire models, leading to large underestimations of burnt area and carbon emissions in the high latitudes. Here, I present a representation of peat fires in the JULES-INFERNO fire model (INFERNO-peat). INFERNO-peat improves the representation of burnt area across the high latitudes, with notable areas of improvement in Canada and Siberia. INFERNO-peat also highlights a large amount of interannual variability in carbon emissions from peat fires. The inclusion of peat fires into JULES-INFERNO demonstrates the importance of representing peat fires in models, and not doing so may heavily restrict our ability to model present and future fires and their impacts across the northern high latitudes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".