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Record W1973356444 · doi:10.1117/1.nph.2.2.020401

Montreal SfNIRS conference shines light on the brain

2015· article· en· W1973356444 on OpenAlexaffabout
Frédéric Lesage, Hellmuth Obrig

Bibliographic record

VenueNeurophotonics · 2015
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsPolytechnique MontréalMontreal Heart Institute
Fundersnot available
KeywordsNeuroimagingNeuroscienceCognitive neuroscienceBrain functionCognitionPsychologyMedicineCognitive scienceData scienceComputer science

Abstract

fetched live from OpenAlex

Noninvasive imaging of brain function has become an indispensable pillar of neuroscience and clinically motivated research on brain disease. Despite the undisputed strength ofMRI-basedtechniques,lessdemandingandbroadlyaffordable methods may be necessary to complement and expand the scope of neuroimaging from a bulky scanner to the bedside or into ecologically meaningful environments. Nearinfrared spectroscopy has clearly developed into one of the most exciting options increasingly used by cognitive and developmental researchers, neonatologists, anesthesiologists, neurologists, and psychiatrists. In concert with electrophysiological and other vascular-based imaging techniques, NIRS may well serve to link basic research to clinical challenges

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)0.004
metaresearch head score (Gemma)0.005
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.129
Threshold uncertainty score0.430

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0030.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0030.003
Scholarly communication0.0090.003
Open science0.0020.004
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.1290.050

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 head0.039
GPT teacher head0.305
Teacher spread0.266 · 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
GenreCommentary

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

Citations0
Published2015
Admission routes2
Has abstractyes

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