Intense forest wildfire sharply reduces mineral soil C and N: the first direct evidence
Bibliographic record
Abstract
Direct evidence of the effects of intense wildfire on forest soil is rare because reliable prefire data are lacking. By chance, an established large-scale experiment was partially burned in the 2002 Biscuit fire in southwestern Oregon. About 200 grid points were sampled across seven burned and seven unburned stands before and after the fire. Fire-related soil changes — including losses of soil organic and inorganic matter — were so large that they became complicated to measure. The 51 Mg·ha –1 of loose rocks on the soil surface after fire suggests erosion of 127 Mg·ha –1 of fine mineral soil, some of which likely left in the fire plume. After accounting for structural changes and erosion with a comparable-layers approach, combined losses from the O horizon and mineral soil totaled 23 Mg C·ha –1 and 690 kg N·ha –1 , of which 60% (C) and 57% (N) were lost from mineral horizons. Applying a fixed-depth calculation — commonly used in previous fire studies — that disregards structural changes and erosion led to underestimates of loss of nearly 50% for C and 25% for N. Although recent debate has centered on the effects of postwildfire forest management on wood, wildlife habitat, and fuels, this study indicates that more consideration should be given to the possible release of greenhouse gases and reduction of future forest productivity and CO 2 uptake.
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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".