Defining thresholds to peat smouldering in the Peat Moisture Code using hydrological modelling 
Bibliographic record
Abstract
The Canadian Forest Fire Danger Rating System (CFFDRS), and in particular the Fire Weather Index System (FWI), are tools used widely across Canada and globally for assessing wildfire potential and predicting wildfire behaviour. While the FWI system has been readily utilized across a number of different forest stand types, the use of the FWI system to represent wildfire potential or behaviour in peatlands has been shown to be less effective, especially in the case of smouldering (flameless) peat fires. This is, in part, due to the wide variation in peat properties and hydrological responses to meteorological forcings between different peatland types and hydrogeological settings within the same region. To begin to address this issue the next generation CFFDRS has incorporated a Peat Moisture Code (PMC) that better represents the ecohydrological feedbacks controlling peatland water table and near-surface moisture responses to fire weather. This new code, however, will still require interpretation based on peatland characteristics to best understand the potential for peatland smouldering fires to initiate and propagate. Here we utilized Hydrus 1-D to model the hydrological response to a drying period across a large range of hypothetical peat property profiles to quantify peat smouldering thresholds and to test the robustness of the PMC. Using the same fire weather inputs used in Hydrus, we determined the daily PMC (and Drought Code) value throughout the drying period. Using the soil water tension and moisture content output by Hydrus and the bulk density with depth input into our Peat Smouldering and Ignition (PSI) model, which uses a thermodynamic approach to predict smouldering propagation, we determined the PMC values that corresponded to varying levels of peat smouldering potential (i.e., surface ignition, moderate smouldering depth, and extreme smouldering depth) across the range of peat profile types. Finally, we mapped typical peatland types onto the “phase space” of peat properties to develop a tool for fire management agencies to best interpret PMC values and the smouldering potential they represent in the various peatlands within their management areas.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".