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Record W2044337661 · doi:10.1117/12.850127

Standoff gas identification and quantification from turbulent stack plumes with an imaging Fourier-transform spectrometer

2010· article· en· W2044337661 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsSNC-Lavalin (Canada)Telus (Canada)
Fundersnot available
KeywordsHyperspectral imagingRadianceImaging spectrometerRemote sensingFull spectral imagingSpectrometerFourier transformEnvironmental scienceStack (abstract data type)PlumeSpectral imagingRange (aeronautics)Trace gasOpticsComputer scienceMaterials sciencePhysicsGeologyMeteorology

Abstract

fetched live from OpenAlex

Benefiting from the rich amount of information provided by a hyperspectral imager such as an imaging Fourier-transform spectrometer, we developed a suite of gas quantification algorithms that were applied to identify the gas released by distant stacks, and to quantify their specific mass flow rates. The method successfully performs the gas quantification through a range of important radiometric and instrumental considerations. Interactions between the released gases and the fluctuating winds result in strong turbulences which are accounted for by a recently developed algorithm avoiding scene change artifacts, thus ensuring valid estimation of the spectral radiance emitted by the plume.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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