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Record W1712892947 · doi:10.3233/bsi-120029

A comparison of spectroscopic techniques for human breath analysis

2012· article· en· W1712892947 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

VenueBiomedical Spectroscopy and Imaging · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
Fundersnot available
KeywordsBreath gas analysisPattern recognition (psychology)Computer scienceArtificial intelligenceBiological systemComputational biologyChemistryChromatographyBiology

Abstract

fetched live from OpenAlex

The analysis of human breath has been driven to new heights and has great potential to impact our society in the area of medical science. Breath analysis is promising as non-invasive, simple and point-of-care clinical measurements to reduce the medical burden caused by invasive, time-consuming and expensive clinical devices. Spectroscopic techniques for breath analysis can offer information to correlate its signals to exhaled substances for molecular identification and quantification to provide the pathophysiological status of the body. In this review paper, techniques such as mass spectrometry-based (gas chromatography-mass spectrometry, proton transfer reaction-mass spectrometry, selected ion flow tube-mass spectrometry), laser absorption spectroscopy-based (cavity ring down spectroscopy and tunable diode laser absorption spectroscopy) and other spectroscopic techniques for breath analysis applications are compared in terms of its advantages/disadvantages, versatilities and plausibility to be transformed in clinical applications.

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 score0.258
Threshold uncertainty score0.536

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 head0.013
GPT teacher head0.330
Teacher spread0.318 · 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