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Record W1502298222 · doi:10.1002/0470027320.s8110

Functional Infrared Imaging for Biomedical Applications

2001· other· en· W1502298222 on OpenAlexaff
Michael Attas

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.

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)0.000
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.067
Threshold uncertainty score0.224

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0670.033

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

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

Citations3
Published2001
Admission routes1
Has abstractyes

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