For peat’s sake! Peat type influences critical moisture thresholds that prevent combustion of organic soils in Western Australia
Bibliographic record
Abstract
Background Preventing ignition of peatlands presents a particular challenge in Western Australia due to a decreasing trend in annual rainfall over the past several decades. Aims We sought to identify critical moisture thresholds and other factors, including chemical composition, geomorphology or peat type that influence the potential for peatlands to sustain smouldering combustion. Methods We wet soil turves from 16 distinct seasonally waterlogged peatlands to pre-determined moisture contents before exposing samples to a heating element to induce smouldering, and then calculated weight and volume loss due to combustion. Other turve portions were used to conduct physical and chemical analyses. Key results Critical moisture thresholds for ignition and combustion varied by peat type due to differences in bulk density and carbon content. Models predicting combustion that contained the explanatory variables peat type, electrical conductivity (EC) of soil and moisture content achieved R-squared values above 0.8. Conclusions Our results indicate the moisture thresholds to prevent ignition of peatlands differ between peat types; knowledge that is important to inform effective decisions made by fire managers during planned fire and bushfire operations. Implications Determining critical moisture thresholds and peat properties that influence peatland flammability informs potential mitigation techniques to reduce the incidence of smouldering peatlands.
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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) | 0.001 | 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".