Biomass burning emission estimates inferred from satellite column measurements of HCHO: Sensitivity to co-emitted aerosol and injection height
Bibliographic record
Abstract
[1] We infer monthly regional biomass burning emissions of formaldehyde (HCHO) during 2006 from space-borne column measurements of HCHO from the SCIAMACHY instrument over Canada, boreal Asia, South America, southern Africa, and Indonesia. We remove the influence of biogenic volatile organic compounds using an offline chemical mechanism. We quantify the sensitivity of our emission estimates to aerosol single scattering albedo, ω, indicative of fresh (ω = 0.8) and aged (ω > 0.9) aerosol, and the relative vertical distribution of the aerosol and HCHO, both which compromise the interpretation of space-based HCHO columns. For our control calculation we assume freshly-emitted gases and aerosols that are mainly confined to the boundary layer. Associated posterior emissions are generally lower than the prior emissions except over Canada and boreal Asia during northern hemisphere summer months. Accounting for faster vertical mixing results in posterior emissions 20%–100% higher than the corresponding control calculation, and consequently more consistent with the prior. Assuming an aged aerosol generally results in a 20% decrease in posterior emissions relative to prior values. Based on the range of posterior estimates from our sensitivity analyses, not accounting for uncertainties associated with the underlying gas-phase and heterogeneous chemistry, we estimate HCHO emission uncertainties are typically 20%–30% but can be up to 300% in extreme cases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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".