MétaCan
Menu
← Back to cohort
Record W4394099278 · doi:10.6084/m9.figshare.12262748

Additional file 3 of T1 mapping performance and measurement repeatability: results from the multi-national T1 mapping standardization phantom program (T1MES)

2020· dataset· en· W4394099278 on OpenAlexaff
Gabriella Captur, Abhiyan Bhandari, Rüdiger Brühl, Bernd Ittermann, Kathryn E. Keenan, Yang Ye, Richard J. Eames, Giulia Benedetti, Camilla Torlasco, Lewis Ricketts, Redha Boubertakh, Nasri Fatih, John P. Greenwood, Leonie E. Paulis, Chris Lawton, Chiara Bucciarelli‐Ducci, Hildo J. Lamb, Richard P. Steeds, Steve Leung, Colin Berry, Sinitsyn Valentin, Andrew Flett, Charlotte de Lange, Francesco De Cobelli, Magalie Viallon, Pierre Croisille, David Higgins, Andreas Greiser, Wenjie Pang, Christian Hamilton‐Craig, W. Strugnell, Tom Dresselaers, Andrea Barison, Dana Dawson, Andrew J. Taylor, François‐Pierre Mongeon, Sven Plein, Daniel Messroghli, Mouaz H. Al‐Mallah, Stuart M. Grieve, Massimo Lombardi, Jihye Jang, Michael Salerno, Nish Chaturvedi, Peter Kellman, David A. Bluemke, Reza Nezafat, Peter Gatehouse, James Moon

Bibliographic record

VenueOpen MIND · 2020
Typedataset
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsStandardizationRepeatabilityImaging phantomComputer scienceMedical physicsNuclear medicineMedicineMathematicsStatisticsOperating system

Abstract

fetched live from OpenAlex

Additional file 3. Supplementary data file reporting all the center-, session- and sequence-specific T1 mapping contributions to the T1MES program.

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)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.504
Threshold uncertainty score0.708

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.5040.110

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 head0.097
GPT teacher head0.322
Teacher spread0.225 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueOpen MIND→Same topicCardiac Imaging and Diagnostics→French-language works237,207→