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Record W4405311848 · doi:10.55458/neurolibre.00029

Integrating Structural, Functional, and BiochemicalBrain Imaging Data with MRShiny Brain - An Interactive WebApplication

2024· preprint· en· W4405311848 on OpenAlexfundno aff
Jessica Archibald, Alexander Mark Weber, Paulina S. Scheuren, Oscar Ortiz, Cassandra M. Choles, Jaimie J. Lee, Niklaus Zölch, Erin L. MacMillan, John L. K. Kramer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Science FoundationConsejo Nacional de Ciencia y TecnologíaInternational Foundation for Research in ParaplegiaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungMichael Smith Health Research BC
KeywordsComputer scienceFunctional Brain ImagingBrain anatomyWorld Wide WebNeuroscienceNeuroimagingPsychologyMedicineMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Archibald et al., (2024). Integrating Structural, Functional, and Biochemical Brain Imaging Data with MRShiny Brain - An Interactive Web Application. NeuroLibre Reproducible Preprints, 29, https://doi.org/10.55458/neurolibre.00029

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)0.002
metaresearch head score (Gemma)0.006
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: Software · Consensus signal: Software
Teacher disagreement score0.076
Threshold uncertainty score0.255

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0760.049

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 head0.037
GPT teacher head0.380
Teacher spread0.343 · 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
GenreSoftware

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

Citations2
Published2024
Admission routes1
Has abstractyes

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