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Record W2077033203 · doi:10.1109/ccece.2008.4564680

An electro-optic hybrid methane sensor

2008· article· en· W2077033203 on OpenAlexvenueno aff
Sergey Zadvornov, Alexander A. Sokolovsky

Bibliographic record

VenueConference proceedings - Canadian Conference on Electrical and Computer Engineering · 2008
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceAbsorption (acoustics)Explosive materialMethaneOptical fiberFiber optic sensorWavelengthOpticsOptoelectronicsPower (physics)InfraredPhysicsChemistry

Abstract

fetched live from OpenAlex

A novel electro-optic hybrid instrument for methane concentration measurements is presented. Compared with conventional fiber optic instruments for methane concentration using infrared absorption in the 1.3 mum and 1.66 mum bands, this instrument overcomes the spectral limitations of optical fibers allowing for the absorption measurements in the 3.3 mum band. Since 3.3 mum is the wavelength corresponding one of the two fundamental frequencies of the methane infrared spectrum, the absorption in this band is significant and consequently the sensorpsilas sensitivity is higher. Furthermore using the higher wavelength decreases the scattering factor so that the influence of the hard impurities in the atmosphere is decreased also. Since the application of the sensor is expected to be in the hazardous and explosive atmospheres some investigations to define the maximum optical power level were performed. Due to the relatively low power level allowed for the explosion safety some special low power consumption algorithms of the signal processing were implemented. Owing to these algorithms the need of ADC and microcomputer is discarded in the remote module. Thus power consumption of the remote module is minimized allowing for the use of cost effective components for optical powering and providing the explosion safety in hazardous areas.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.931
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.014
GPT teacher head0.212
Teacher spread0.198 · 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.

Study designSimulation or modeling
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

Citations3
Published2008
Admission routes1
Has abstractyes

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