Influences of boreal fire emissions on Northern Hemisphere atmospheric carbon and carbon monoxide
Bibliographic record
Abstract
There were large interannual variations in burned area in the boreal region (ranging between 3.0 and 23.6 × 10 6 ha yr −1 ) for the period of 1992 and 1995–2003 which resulted in corresponding variations in total carbon and carbon monoxide emissions. We estimated a range of carbon emissions based on different assumptions on the depth of burning because of uncertainties associated with the burning of surface‐layer organic matter commonly found in boreal forest and peatlands, and average total carbon emissions were 106–209 Tg yr −1 and CO emissions were 33–77 Tg CO yr −1 . Burning of ground‐layer organic matter contributed between 46 and 72% of all emissions in a given year. CO residuals calculated from surface mixing ratios in the high Northern Hemisphere (HNH) region were correlated to seasonal boreal fire emissions in 8 out of 10 years. On an interannual basis, variations in area burned explained 49% of the variations in HNH CO, while variations in boreal fire emissions explained 85%, supporting the hypotheses that variations in fuels and fire severity are important in estimating emissions. Average annual HNH CO increased by an average of 7.1 ppb yr −1 between 2000 and 2003 during a period when boreal fire emissions were 26 to 68 Tg CO −1 higher than during the early to mid‐1990s, indicating that recent increases in boreal fires are influencing atmospheric CO in the Northern Hemisphere.
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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 | 0.000 | 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".