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Record W2009403771 · doi:10.4015/s1016237209001568

RECOGNITION OF VOLATILE ORGANIC COMPOUNDS UTILIZING A PORTABLE ELECTRONIC NOSE

2009· article· en· W2009403771 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 Engineering Applications Basis and Communications · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsNational Research Council Canada
FundersNational Taiwan University
KeywordsElectronic noseMicrocontrollerSensor arrayEmbedded systemSensitivity (control systems)Computer hardwareComputer scienceMaterials scienceChemistryEngineeringArtificial intelligenceElectronic engineering

Abstract

fetched live from OpenAlex

In this study, we proposed a portable electronic nose (e-nose) system based on a microcontroller (MSP430-FG439) combined with a tin oxide ( SnO 2 ) gas sensor, which was heated by a cyclic heating method, to recognize the volatile organic compounds (VOCs). We had demonstrated that this e-nose system can classify and quantify VOCs, such as methanol and ethanol. The sensitivity of the e-nose system had good linearity in the concentration range of 10–40 ppm of these two VOCs, respectively. This portable e-nose system was implemented with a microcontroller acted as CPU, an LCD for displaying information of gases in real time, a wireless communication system, ZigBee, and a warning system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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