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Record W1502298222 · doi:10.1002/0470027320.s8110

Functional Infrared Imaging for Biomedical Applications

2001· other· en· W1502298222 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

VenueHandbook of Vibrational Spectroscopy · 2001
Typeother
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsNational Research Council Institute for Biodiagnostics
Fundersnot available
KeywordsBlood oxygenationOxygenationDeoxygenated HemoglobinNear-infrared spectroscopyBiomedical engineeringMagnetic resonance imagingAbsorption (acoustics)Functional magnetic resonance imagingNuclear magnetic resonanceHemoglobinChemistryMaterials scienceNeuroscienceMedicineRadiologyBiologyInternal medicinePhysicsBiochemistry

Abstract

fetched live from OpenAlex

Abstract Blood oxygenation is a key measure of the physiological activity of an organ or tissue at any given time. Near‐infrared (NIR) spectroscopy and spectroscopic imaging of tissue can provide blood oxygenation information based on differences in absorption spectra of oxygenated and deoxygenated hemoglobin. Real‐time, in vivo NIR spectroscopic imaging of tissue can generate maps showing changes in activity as a function of time and location. The technique has been used to image the brain in action (with capabilities complementary to those of magnetic resonance imaging) and to study the response of skin to damage and disease. This article discusses instrumentation and methods, reviews NIR imaging studies of the brain, skin, and other organs (to 1999 December), and speculates on future developments.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.144
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.013
GPT teacher head0.308
Teacher spread0.296 · 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