{"id":"W2903516807","doi":"10.1101/484543","title":"Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Université de Sherbrooke","funders":"National Center for Advancing Translational Sciences; China Scholarship Council; National Center for Research Resources; National Natural Science Foundation of China; National Institutes of Health; Vanderbilt University","keywords":"Reproducibility; Tractography; Diffusion MRI; Computer science; Outlier; Imaging phantom; Tracking (education); Ground truth; Diffusion; Artificial intelligence; Magnetic resonance imaging; Pattern recognition (psychology); Data mining; Nuclear medicine; Statistics; Mathematics; Psychology; Medicine; Physics; Radiology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1656033,0.001464519,0.002379843,0.00303412,0.003110291,0.006723795,0.004207982,0.004561032,0.002950303],"category_scores_gemma":[0.3997363,0.0008412789,0.002270313,0.002986118,0.004179534,0.004388791,0.007384312,0.003460078,0.002323628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002823053,"about_ca_system_score_gemma":0.01143829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007344407,"about_ca_topic_score_gemma":0.007560954,"domain_scores_codex":[0.8800824,0.05972382,0.01407784,0.015402,0.02895412,0.001759792],"domain_scores_gemma":[0.4485789,0.3486317,0.02997517,0.06141591,0.1002149,0.0111834],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003256568,0.000895649,0.08002865,0.009147608,0.002589203,0.001643143,0.007537031,0.02413412,0.008565714,0.02906927,0.3603778,0.4727553],"study_design_scores_gemma":[0.00153597,0.003271463,0.1191261,0.007762089,0.001376941,0.009062084,0.006034025,0.1070613,0.02476863,0.1301616,0.5887933,0.001046582],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2999389,0.02528885,0.4684833,0.1215053,0.01583453,0.004868371,0.03570223,0.008803592,0.01957497],"genre_scores_gemma":[0.5665223,0.004131225,0.3266091,0.0165009,0.006827654,0.007960496,0.05362502,0.007664979,0.01015839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8343967,"threshold_uncertainty_score":0.8758042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1401249313870956,"score_gpt":0.3588019647956082,"score_spread":0.2186770334085126,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}