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Record W2002667671 · doi:10.1063/1.2711537

Simultaneous detection and identification of multigas pollutants using filament-induced nonlinear spectroscopy

2007· article· en· W2002667671 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

VenueApplied Physics Letters · 2007
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsDefence Research and Development CanadaUniversité Laval
Fundersnot available
KeywordsFilamentationFemtosecondTrace gasMethaneAnalytical Chemistry (journal)AcetyleneLaser-induced fluorescenceProtein filamentSpectroscopyLaserFluorescence spectroscopyChemistryFluorescenceAtomic physicsMaterials scienceMolecular physicsOpticsEnvironmental chemistryPhysics

Abstract

fetched live from OpenAlex

The authors report on an approach for simultaneous monitoring of multigas pollutants based on fluorescence emission of trace gases, induced by the filamentation of intense femtosecond laser pulses in air. The high intensity inside a filament can dissociate the gas molecules into small fragments which emit characteristic fluorescence. This method is illustrated for simultaneously sensing atmospheric trace gases, methane and acetylene. The spectra of an “unknown” mixture were analyzed by using a genetic algorithm, showing good concentration agreement with the experimental results within an error of 25%.

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 score0.053
Threshold uncertainty score0.789

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 communication0.0000.000
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 head0.012
GPT teacher head0.265
Teacher spread0.253 · 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