Measurement of transmissivity in outdoor soot plumes using sky-scattered solar radiation
Bibliographic record
Abstract
Currently there is a lack of practical approaches for quantifying soot emission rates from unconfined industrial sources such as stack plumes and flares. Most regulatory standards are based on a human-observed opacity standard as outlined in U.S. Environmental Protection Agency Method 9. This paper is the third part of an investigation of a sky-scattered solar radiation based optical diagnostic for plume transmissivity measurement. The technique is based on the established laboratory optical technique known as diffuse Two Dimensional Line-of-sight attenuation (2D-LOSA), in which images of a light source, the light source through a plume, and plume alone are compared in a so-called “3-image” algorithm. However, when the technique is transferred to an outdoor setting where the light source is sky-scattered solar radiation, a “1-image” algorithm must be used, in which the unattenuated light in the plume region must be interpolated from other regions of the image. A detailed explanation of 2D-LOSA 3-image vs. 1-image routines for lab and outdoor measurements can be found in previous papers. This paper is focused on 1) background interpolation analysis under different sky conditions to determine unattenuated intensities behind the plume; 2) comparison of measurements of soot in an unconfined plume using both lab-based and sky-LOSA techniques; and 3) determination of the ultimate accuracy and sensitivity limits in terms of soot emission rate for the sky-LOSA technique.
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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 | 0.000 | 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".