Impacts of wildfire on effective sediment particle size : implications for post-fire sediment budgets
Bibliographic record
Abstract
Recent work in an Australian catchment has shown that severe wildfires followed by rainstorm events lead to redistribution of topsoil and export of sediment to the local river network. Considering the affinity of nutrients and other contaminants for fine sediment, and the potential for considerable downstream impacts, a sound understanding of the fine-sediment delivery from burnt systems is required. However, past work has shown that fire can modify the particle size distribution of the soil through formation of robust sand-sized aggregates comprising fine clays and silts. Image analysis confirmed the presence of fire-modified soil aggregates in our study area. Analysis of aggregate form and fluvial behaviour showed that fire-modified aggregates are fused, dense and inorganic in nature with settling velocities of an order of magnitude faster than unburnt soil aggregates or classic riverine flocs. This implies an increased potential for storage of nutrient-rich fine sediment within slope units, flood plains and river channels. The unusual behaviour of these composite particles should be considered in the construction of post-fire fine-sediment 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".