Black carbon and organic arbon in aerosol particles from crown fires in the Canadian boreal forest
Bibliographic record
Abstract
[1] In the boreal forest, high-intensity crown fires account for an overwhelming proportion of the area burned yearly. Quantifying the amount of black carbon (BC) from boreal crown fires in Canada is essential for assessing the effect on regional climate from natural wildfire aeros l emissions versus that from anthropogenic activities. This is particularly relevantbecause c imate change will ikely ead to increased wildfire activity in northern Canada. During 4-5 July 1998, two controlled fires in Northwest Territories, Canada, were conducted as part of the International Crown Fire Modeling Experiment. We report here the BC and organic carbon (OC) compositions of aerosols produced during the flaming and smoldering stages of burning. Particles were collected on back-to-back quartz-fiber filters by helicopter with a hi-vol sampler and at ground level with a dichotomous sampler to separate the fine (≤2.5 µm diameter) and coarse (2.5-10 µm diameter) particle fractions. An analysis of the back filter in relation to the front filter from the dichot sampler for both the fine and coarse fractions provided a means to correct for the adsorption of gas-phase organic compounds on filters (positive artifact) and for the loss of particulate carbon from filters by volatilization (negative artifact). BC and OC masses, which combine here to give total carbon (TC), were determined by the thermal-optical method. The BC to TC ratio for the flaming stage was 0.085
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".