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Record W2018952802 · doi:10.1063/1.1364670

Infrared radiometry-based background-compensated thermometric instrument for noncontact temperature and friction measurements

2001· article· en· W2018952802 on OpenAlexaff
L. Li, Andreas Mandelis, José Ángel García García, C. Eccles

Bibliographic record

VenueReview of Scientific Instruments · 2001
Typearticle
Languageen
FieldEngineering
TopicCalibration and Measurement Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceOpticsChopperBlack-body radiationRadiometryInfraredAmplifierFigure of meritTemperature measurementCeramicLaserRadiationOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

The design and performance of a novel thermometric instrument featuring thermalemission-intensity harmonic modulation, noncontact infrared radiometric detection, and stray background suppression is described. The instrumental principle depends on thermal (blackbody) emission of Planck radiation from a heated surface. It was developed to measure small temperature rises caused by frictional heating. A low-power He–Ne heating laser was used to investigate the sensitivity and estimate a figure-of-merit (FOM) for the instrument. Background compensation leading to signal baseline suppression was partly achieved with a differential mechanical chopper blade, designed to induce destructive interference of infrared radiation superposition from heated and reference spots on a ceramic sample coated with a metallic thin film. Additional background suppression was achieved by lock-in amplifier signal amplitude and phase compensation through an externally superposed wave at the same chopping frequency. The FOM of the noncontact thermometric instrument was 159.9±8.5. The system sensitivity (minimum temperature rise) for the particular thin-film/ceramic material was estimated to be 0.18–0.23 °C.

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)0.001
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 score0.436
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.055
GPT teacher head0.273
Teacher spread0.219 · 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

Citations2
Published2001
Admission routes1
Has abstractyes

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