Fire effects on soil organic matter content, composition, and nutrients in boreal interior Alaska
Bibliographic record
Abstract
Boreal ecosystems contain a substantial fraction of the earth's soil carbon stores and are prone to frequent and severe wildfires. In this study, we examine changes in element and organic matter stocks due to a 1999 wildfire in Alaska. One year after the wildfire, burned soils contained between 1071 and 1420 g/m 2 less carbon than unburned soils. Burned soils had lower nitrogen than unburned soils, higher calcium, and nearly unchanged potassium, magnesium, and phosphorus stocks. Burned surface soils tended to have higher concentrations of noncombustible elements such as calcium, potassium, magnesium, and phosphorus compared with unburned soils. Combustion losses of carbon were mostly limited to surface dead moss and fibric horizons, with no change in the underlying mineral horizons. Burning caused significant changes in soil organic matter structure, with a 12% higher ratio of carbon to combustible organic matter in surface burned horizons compared with unburned horizons. Pyrolysis gas chromatography mass spectroscopy also shows preferential volatilization of polysaccharide-derived organic matter and enrichment of lignin- and lipid-derived compounds in surface soils. The chemistry of deeper soil layers in burned and unburned sites was similar, suggesting that immediate fire impacts were restricted to the surface soil horizon.
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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) | 0.000 | 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".