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Record W2087446056 · doi:10.1117/12.817905

A new generation of dual input beam spectroradiometers for the standoff detection of chemicals

2009· article· en· W2087446056 on OpenAlexaffabout
Louis Moreau, Claude Roy, Christian Vallières, Luc Lévesque, Marc‐André Soucy, Eldon Puckrin, Hugo Lavoie, Jean‐Marc Thériault

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsDefence Research and Development CanadaABB (Canada)
Fundersnot available
KeywordsSpectroradiometerComputer scienceRadianceSoftwareRemote sensingCalibrationOpticsPhysics

Abstract

fetched live from OpenAlex

A new generation of field-rated optical subtraction instruments for the standoff detection of chemicals will be presented. It combines the latest ABB Bomem spectroradiometer technology and software with the concepts used in the design of the original instrument designed and manufactured more than 12 years ago by ABB and Defence Research and Development Canada - Valcartier. This instrument is a Fourier-transform spectroradiometer with dual input beams. It is a passive, stand-off sensor that uses one input port to interrogate a scene under investigation while the other input beam can be pointed at the background scene. The instrument automatically measures the difference of spectral radiance between the target and the background scenes by optical subtraction, hence achieving a real-time suppression of the background signal. The resulting measurement is the unique spectral signature of the target measured in real time. The system includes a software module to control the instrument and the acquisition parameters, a module for the radiometric calibration and a module to perform the identification and quantification, in real time, of various gases. An overview of the instrument design and initial results of tests are presented.

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)0.000
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 score0.103
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.242
Teacher spread0.229 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicSpectroscopy and Laser ApplicationsFrench-language works237,207