Click Here for Full Article Inventory of boreal fire emissions for North America in 2004: Importance of peat burning and pyroconvective injection
Bibliographic record
Abstract
[1] The summer of 2004 was one of the largest fire seasons on record for Alaska and western Canada. We construct a daily bottom-up fire emission inventory for that season, including consideration of peat burning and high-altitude (buoyant) injection, and evaluate it in a global chemical transport model (the GEOS-Chem CTM) simulation of CO through comparison with MOPITT satellite and ICARTT aircraft observations. The inventory is constructed by combining daily area burned reports and MODIS fire hot spots with estimates of fuel consumption and emission factors based on ecosystem type. We estimate the contribution from peat burning using drainage and peat distribution maps for Alaska and Canada; 17 % of the reported 5.1 10 6 ha burned were located in peatlands in 2004. Our total estimate of North American fire emissions during the summer of 2004 is 30 Tg CO, including 11 Tg from peat. Including peat burning in the GEOS-Chem simulation improves agreement with MOPITT observations. The long-range transport of fire plumes observed by MOPITT suggests that the largest fires injected a significant fraction of their emissions in the upper troposphere. Citation: Turquety, S., et al. (2007), Inventory of boreal fire emissions for North America in 2004: Importance of peat burning and
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.011 | 0.002 |
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".