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Record W4391448089 · doi:10.55458/neurolibre.00023

Paper is not enough: Crowdsourcing the T1 mappingcommon ground via the ISMRM reproducibility challenge

2024· preprint· en· W4391448089 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
FieldEnvironmental Science
TopicSoil Geostatistics and Mapping
Canadian institutionsSunnybrook HospitalMcGill UniversityPhilips (Canada)Hôpital Maisonneuve-RosemontMcGill University Health CentreUniversity of British Columbia
Fundersnot available
KeywordsCrowdsourcingReproducibilityCommon groundData scienceComputer scienceStatisticsPsychologyMathematicsWorld Wide WebSocial psychology

Abstract

fetched live from OpenAlex

Boudreau et al., (2024). Paper is not enough: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge. NeuroLibre Reproducible Preprints, 23, https://doi.org/10.55458/neurolibre.00023

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)0.053
metaresearch head score (Gemma)0.251
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 score0.947
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.251
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.004
Science and technology studies0.0050.007
Scholarly communication0.0150.011
Open science0.0040.016
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0190.019

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 head0.035
GPT teacher head0.259
Teacher spread0.224 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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Same topicSoil Geostatistics and MappingFrench-language works237,207