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Record W2786214149

Measuring Blood Glucose Using Vertical Cavity Semiconductor Lasers (VCSELs)

2007· article· en· W2786214149 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

VenueCMBES Proceedings · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLaserOptoelectronicsSemiconductor laser theoryNoise (video)Materials scienceBlood glucose monitoringAbsorption (acoustics)Vertical-cavity surface-emitting laserWavelengthOpticsSemiconductorComputer scienceMedicineDiabetes mellitusPhysicsArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

As diabetes mellitus is becoming a more widespread serious disease, a more convenient and ac- curate way of controlling blood glucose, which improves the patient’s life quality and adds savings for health care systems, is desirable. Optical methods are one of the painless and promising methods that can be used for blood glucose predictions. However, having accuracies lower than what is acceptable clinically has been a major concern. To improve on the accuracy of the predictions, the signal-to- noise ratio in the spectrum can be increased, for which the use of thermally tunable vertical cavity semiconductor lasers (VCSEL) is proposed. This paper will present and discuss the results of applying Partial Least Square (PLS) techniques on small wavelength windows with the goal of determining the number of VCSELs required to predict glucose concentration, and verify that with PLS it is possible to predict glucose concentration from a selected subset of absorption spectra.

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)0.001
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 score0.008
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.034
GPT teacher head0.318
Teacher spread0.284 · 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