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Record W2076714610 · doi:10.1366/000370208786049132

How to Deal with Some Spurious Fringes in Fourier Transform Infrared Spectrometers

2008· article· en· W2076714610 on OpenAlexaff
Jean-Joseph Ma×, Camille Chapados

Bibliographic record

VenueApplied Spectroscopy · 2008
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Chemometric Analyses
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité du Québec à Montréal
Fundersnot available
KeywordsSpurious relationshipSpectrometerInfraredFourier transformOpticsFourier transform spectroscopyFourier transform infrared spectroscopyInterferometrySpectrum (functional analysis)PhysicsMaterials scienceComputer science

Abstract

fetched live from OpenAlex

Faulty fringes coming from an infrared spectrometer may creep into a spectrum. Because these come from one faulty interferogram out of many used to obtain the spectrum, these may pass unnoticed. However, they cause some problems in the data treatment of factor analysis and other spectral analysis. We present a method for detecting the faulty fringes and give a simple method to eliminate them at the interferogram accumulation level.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.021
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.004

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 head0.011
GPT teacher head0.227
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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
Published2008
Admission routes1
Has abstractyes

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