Controls on carbon consumption during Alaskan wildland fires
Bibliographic record
Abstract
Abstract A method was developed to estimate carbon consumed during wildland fires in interior Alaska based on medium‐spatial scale data (60 m cell size) generated on a daily basis. Carbon consumption estimates were developed for 41 fire events in the large fire year of 2004 and 34 fire events from the small fire years of 2006–2008. Total carbon consumed during the large fire year (2.72 × 106 ha burned) was 64.7 Tg C, and the average carbon consumption during the small fire years (0.09 × 106 ha burned) was 1.3 Tg C. Uncertainties for the annual carbon emissions ranged from 13% to 21%. Carbon consumed from burning of black spruce forests represented 76% of the total during large fire years and 57% during small fire years. This was the result of the widespread distribution of black spruce forests across the landscape and the deep burning of the surface organic layers common to these ecosystems. Average carbon consumed was 3.01 kg m−2 during the large fire year and 1.69 kg m−2 during the small fire years. Most of the carbon consumption was from burning of ground layer fuels (85% in the large fire year and 78% in small fire years). Most of the difference in average carbon consumption between large and small fire years was in the consumption of ground layer fuels (2.60 vs. 1.31 kg m−2 during large and small fire years, respectively). There was great variation in average fuel consumption between individual fire events (0.56–5.06 kg m−2) controlled by variations in fuel types and topography, timing of the fires during the fire season, and variations in fuel moisture at the time of burning.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".