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Record W3083554083 · doi:10.21105/joss.02343

qMRLab: Quantitative MRI analysis, under one umbrella

2020· article· en· W3083554083 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Journal of Open Source Software · 2020
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsCentre Intégré de Santé et Services Sociaux de Chaudière-AppalacheUniversity of CalgaryCentre intégré de santé et de services sociaux de Chaudière-AppalachesMcGill UniversityUniversité de MontréalPolytechnique MontréalMontreal Neurological Institute and HospitalMontreal Heart Institute
FundersNatural Sciences and Engineering Research Council of CanadaFondation Brain CanadaInstitut de Cardiologie de MontréalFondation Institut de Cardiologie de MontréalCanada First Research Excellence FundRéseau en Bio-Imagerie du Quebec
KeywordsMedicineComputer science

Abstract

fetched live from OpenAlex

Magnetic resonance imaging (MRI) has revolutionized the way we look at the human body. However, conventional MR scanners are not measurement devices. They produce digital images represented by "shades of grey", and the intensity of the shades depends on the way the images are acquired. This is why it is difficult to compare images acquired at different clinical sites, limiting the diagnostic, prognostic, and scientific potential of the technology.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.774
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.199
GPT teacher head0.421
Teacher spread0.222 · 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