MétaCan
Menu
Back to cohort
Record W7039436906

Measurement of transmissivity in outdoor soot plumes using sky-scattered solar radiation

2008· article· en· W7039436906 on OpenAlexfundvenueno aff

Bibliographic record

VenueNPARC · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Toxins and Detection Methods
Canadian institutionsnot available
FundersCanadian Association of Petroleum Producers
KeywordsSootPlumeRadiationOpacityAttenuationSkyDiffuse sky radiationRadiant intensity
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.045
GPT teacher head0.266
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueNPARCSame topicMarine Toxins and Detection MethodsFrench-language works237,207