Fine‐scale distribution of moisture in the surface of a degraded blanket bog and its effects on the potential spread of smouldering fire
Bibliographic record
Abstract
Abstract During rain‐free periods, the water table in peatlands falls and the moisture content (MC) of top 10 cm, including the moss layer, depends upon the hydrophysical properties and the responses of the local composition of species to water deficit. Thus, the ecosystem becomes vulnerable to surface peat fires. The spread of such fires is often irregular; however, there is little understanding of how the fine‐scale variation of peatMCcan affect the spread of smouldering fire. We analyse the fine‐scale distribution of surfaceMCfrom a degraded blanket bog (i.e., horizontal intervals of 10 cm), thus indirectly analysing the effect of fine‐scaleMCdistribution on peat fire spread. We determine the relationship of vegetation and microtopography to the spatial distribution of near surfaceMCand the moisture gradient around patches of dry peat (less than 250%MC, moisture content in a dry mass basis). We found that theMCof the surface peat was distributed in clusters with dry patches (less than 250%MC) averaging 40 ± 15 cm in size. TheMCgradient surrounding dry patches was 10 ± 7%MC∙cm−1. A mixed‐effects model showed that dry patches were associated with lawns (of feather moss orSphagnum) and hummocks of feather moss. Our model showed that wet patches were associated with both hummocks and hollows ofSphagnum. Therefore, the characterization of the surfaceMCin a fine‐scale is critical if we are to understand the spread of fires in 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 | 0.000 |
| 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".