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Record W3009018113

Quantitative MRI of the spinal cord: Reproducibility and normative values across 40 sites

2020· article· en· W3009018113 on OpenAlexaff
Eva Alonso‐Ortiz, Charley Gros, Alexandru Foias, Mihael Abramovic, Christoph Arneitz, Nicole Atcheson, Laura Barlow, Robert Barry, Markus Barth, Marco Battiston, Christian Büchel, Matthew D. Budde, Virginie Callot, Benjamin De Leener, Maxime Descoteaux, Paulo Loureiro de Sousa, Marek Dostál, Julien Doyon, Adam Dvorak, Falk Eippert, Karla R. Epperson, Jürgen Finsterbusch, Issei Fukunaga, Claudia A. M. Gandini Wheeler‐Kingshott, Giancarlo Germani, Guillaume Gilbert, Francesco Grussu, Akifumi Hagiwara, Pierre‐Gilles Henry, Thomas Horak, Masaaki Hori, James M. Joers, Kouhei Kamiya, Haleh Karbasforoushan, Ali Khatibi, Joo-Won Kim, Nawal Kinany, Hagen H. Kitzler, Shannon Kolind, Joe Yazhuo Kong, Paul Kuntke, Nyoman D. Kurniawan, Sławomir Kuśmia, René Labounek, Maria Marcella Laganà, Corree Laule, Christine Law, Christophe Lenglet, Tobias Leutritz, Yaou Liu, Sara Llufriú, Sean Mackey, Eloy Jiménez Martínez, Igor Nestrašil, Nico Papinutto, Daniel S. Papp, Deborah Pareto, Todd B. Parrish, Anna Pichiecchio, À. Rovira Cañellas, Marc J. Ruitenberg, Rebecca S. Samson, G. Savini, Maryam Seif, Alan C. Seifert, Alex K. Smith, Zachary A. Smith, Elisabeth Solana, Yuichi Suzuki, George Tackley, Alexandra Tinnermann, Jan Valošek, Marios Yiannakas, Kenneth A. Weber, Nikolaus Weiskopf, Richard G. Wise, Patrik O. Wyss, Junqian Xu, Julien Cohen‐Adad

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2020
Typearticle
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsMontreal Neurological Institute and HospitalUniversité de SherbrookeMcGill UniversityUniversity of British ColumbiaPolytechnique Montréal
Fundersnot available
KeywordsReproducibilityNormativeMedicineMathematicsStatisticsPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.021
GPT teacher head0.277
Teacher spread0.255 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations1
Published2020
Admission routes1
Has abstractno

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