Structural characteristics and behavior of fire‐modified soil aggregates
Bibliographic record
Abstract
The transport dynamics of burnt material in fluvial systems require attention since the off‐site transfer of soil material from wildfire‐affected slopes includes ash and nutrients which may have negative impacts on downstream water quality. The aim of this work is to explore the presence, structural characteristics, and fluvial behavior of fire‐modified soil aggregates in burnt soil material sampled from a water supply catchment near Sydney, Australia, and to compare their fluvial behavior to that of composite particles from unburnt soils. Samples of burnt and unburnt soil material were analyzed for effective particle size, settling velocity, composite particle density, and porosity using established microscope‐based image analysis approaches. Burnt soil aggregates exhibit significantly higher settling velocities (mean 11.47 ± 1.11 mm s−1) than unburnt particles of similar diameter (3.36 ± 0.91 mm s−1) reflecting an increase in density because of reduction in organic content and a reduction in pore space linked to shrinkage. Soil aggregates in severely burnt soil are dense and inorganic and behave differently to their unburnt counterparts and discrete grains of the same size. Burnt composite particles are robust and readily transported within fluvial systems. Their presence in burnt soil and mobilized material is likely to have important implications for postfire fine (<63 μm) sediment and nutrient budgets.
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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 | 0.001 | 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) | 0.000 | 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".