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Record W2488930730 · doi:10.1063/1.4958820

Frequency analysis of temperature-dependent interferometric signal for the measurement of the temperature coefficient of refractive index

2016· article· en· W2488930730 on OpenAlexaff
Jianqin Zhou, Jun Shen, W. Stuart Neill

Bibliographic record

VenueReview of Scientific Instruments · 2016
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsNational Research Council Canada
FundersOffice of International Science and Engineering
KeywordsRefractive indexInterferometryMaterials scienceTemperature measurementIndex (typography)OpticsTemperature coefficientSIGNAL (programming language)PhysicsThermodynamics

Abstract

fetched live from OpenAlex

A method of frequency analysis for the measurement of the temperature coefficient of refractive index (dn/dT) using a Fabry-Perot interferometer was developed and tested against ethanol and water. The temperature-dependent interferometric signal described by Airy's formula was analyzed in both the temperature and frequency domains. By fast Fourier transform, a low-pass filter was designed and employed to eliminate the noise superimposed on the signal. dn/dT was determined accurately from the noise-removed signal by peak analysis. Furthermore, the signal frequency parameters may be utilized for the material thermophysical property characterization. This method lays the foundation for an online dn/dT instrument for monitoring chemical processes.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.067
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.035
GPT teacher head0.280
Teacher spread0.245 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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