A Generalized Sky-LOSA Method to Quantify Soot/Black Carbon Emission Rates in Atmospheric Plumes of Gas Flares
Bibliographic record
Abstract
A new generalized theory governing sky-LOSA measurements (line-of-sight attenuation measurements of sky-light) of soot mass flux in atmospheric plumes has been developed which enables accurate measurements in the presence of in-scattered light from the sky and sun. The new approach is quantitatively tested using field measurement data collected for a gas flare at a turbocompressor station in Mexico. Although the soot plume of the tested flare was on the threshold of visible to the naked eye, the sensitivity of the current hardware was more than sufficient to resolve the soot mass emission rate of 0.067 g/s, with a quantified 95% confidence interval of 0.050 to 0.090 g/s. Results of a Monte Carlo simulation showed that soot optical property uncertainty was the major contributor to the overall measurement uncertainty. By contrast, correction of in-scattering via the generalized theory was a comparatively minor contributor, and was specifically insensitive to assumptions about the sky polarization state and intensity distribution. Given the prevalence of flaring and its implication as a potentially critical source of black carbon emissions, sky-LOSA is an essential new technology to directly quantify the impact of these globally distributed sources, for which comparable technologies do not exist. Copyright 2013 American Association for Aerosol Research
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".