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Record W4387450072 · doi:10.55458/neurolibre.00014

Results of the ISMRM 2020 joint Reproducible Research& Quantitative MR study groups reproducibility challenge on phantomand human brain T1 mapping

2023· preprint· en· W4387450072 on OpenAlexaff
Mathieu Boudreau, Agâh Karakuzu, Julien Cohen‐Adad, Ecem Bozkurt, Madeline Carr, Marco Castellaro, Luis Concha, Mariya Doneva, Seraina A. Dual, Alex Ensworth, Alexandru Foias, Véronique Fortier, Refaat E. Gabr, Guillaume Gilbert, Carri Glide‐Hurst, Matthew Grech‐Sollars, Siyuan Hu, Oscar Jalnefjord, Jorge Jovicich, Kübra Keskin, Peter Koken, Anastasia Kolokotronis, Simran Kukran, Nam G. Lee, Ives R. Levesque, Bochao Li, Dan Ma, Burkhard Mädler, Nyasha G. Maforo, Jamie Near, Erick H. Pasaye, Alonso Ramírez-Manzanares, Ben Statton, Christian Stehning, Stefano Tambalo, Ye Tian, Chenyang Wang, Kilian Weis, Niloufar Zakariaei, Shuo Zhang, Ziwei Zhao, Nikola Stikov

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsMcGill UniversityMcGill University Health CentreSunnybrook HospitalPolytechnique MontréalPhilips (Canada)Montreal Heart InstituteUniversity of British ColumbiaUniversité de MontréalMila - Quebec Artificial Intelligence InstituteInstitut Universitaire de Gériatrie de MontréalCentre Hospitalier Universitaire Sainte-JustineHôpital Maisonneuve-Rosemont
FundersNational Institute of Standards and Technology
KeywordsReproducibilityImaging phantomJoint (building)Nuclear medicineMedical physicsHuman brainMedicineBiomedical engineeringPsychologyNeuroscienceMathematicsEngineeringStatistics

Abstract

fetched live from OpenAlex

Boudreau et al., (2023). Results of the ISMRM 2020 joint Reproducible Research & Quantitative MR study groups reproducibility challenge on phantom and human brain T1 mapping. NeuroLibre Reproducible Preprints, 14, https://doi.org/10.55458/neurolibre.00014

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)0.059
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.311

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.059
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0030.007
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.0100.010

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 head0.471
GPT teacher head0.505
Teacher spread0.033 · 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.

Study designObservational
DomainReproducibility
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

Citations4
Published2023
Admission routes1
Has abstractyes

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