Tropical peat composition may provide a negative feedback on fire occurrence and severity
Bibliographic record
Abstract
Abstract Loss of peat through increased burning will have major impacts on the global carbon cycle. In a normal hydrological state, the risk of fire propagation is largely controlled by peat bulk density and moisture content. However, where humans have interfered with the moisture status of peat either via drainage, or indirectly via climate change, we hypothesise that its botanical composition will become important to flammability, such that peats from different latitudes might have different compositionally-driven susceptibility to ignition. We use pyrolysis combustion flow calorimetry to determine the temperature of maximum thermal decomposition (Tmax) of peats from different latitudes, and couple this to a botanical composition analysis. We find that tropical peat has higher Tmax than other regions, likely on account of its higher wood content which appears to convey a greater resistance to ignition. This resistance also increases with depth, which means that loss of surface peat in tropical regions may lead to a reduction in the subsequent ignitability of deeper peat layers as they are exposed, potentially resulting in a negative feedback on increased fire occurrence and severity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".