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Record W2108489616 · doi:10.1364/boe.3.000064

Independent component analysis of broadband near-infrared spectroscopy data acquired on adult human head

2011· article· en· W2108489616 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

VenueBiomedical Optics Express · 2011
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFunctional near-infrared spectroscopyIndependent component analysisSpectroscopyComponent (thermodynamics)OpticsBiomedical engineeringAbsorption (acoustics)Near-infrared spectroscopyMaterials scienceMedicineComputer sciencePhysicsNeuroscienceArtificial intelligenceBiology

Abstract

fetched live from OpenAlex

The goal of this study was to investigate the ability of independent component analysis in the time-spectral domain to isolate physiological sources of functional near infrared spectroscopy signals. We apply independent component analysis to the broadband fNIRS data acquired on the human forehead at 650 different wavelengths between 700 nm and 950 nm. To induce cerebral oxygenation changes we use the breath holding paradigm. We found one major independent component during baseline and two major components during exercise. Each independent component corresponds to one oxy-hemoglobin and one deoxy-hemoglobin time courses. The corresponding characteristic spectra of changes in optical absorption suggested that one component represented vasodilation of cerebral arterioles while the delayed component represented the washout of deoxyhemoglobin either in cerebral capillaries and venules or in extra cerebral tissue. We found that both broadband and isolated wavelength data can produce similar independent components.

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)0.000
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 score0.218
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.356
Teacher spread0.294 · 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