{"id":"W3094067744","doi":"10.1016/j.mri.2020.10.007","title":"Classifying MRI motion severity using a stacked ensemble approach","year":2020,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Philips (Canada); University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion (physics); Artificial intelligence; Computer science; Computer vision; Magnetic resonance imaging; Workflow; Image quality; Displacement (psychology); Rotation (mathematics); Pattern recognition (psychology); Radiology; Medicine; Image (mathematics); Psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008834149,0.0001586064,0.0002327785,0.0000419163,0.0001372025,0.00003400638,0.00009929052,0.00004178102,0.00004915134],"category_scores_gemma":[0.00004846563,0.000159134,0.00006948983,0.0003532521,0.00008455232,0.0001324983,0.00007446001,0.0002454461,0.00001240865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008028944,"about_ca_system_score_gemma":0.00004707135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002566329,"about_ca_topic_score_gemma":3.301002e-7,"domain_scores_codex":[0.9987967,0.00002262744,0.0002482652,0.0004205207,0.0002118542,0.0003000604],"domain_scores_gemma":[0.9993863,0.00001720299,0.00007412907,0.0002885879,0.00007790828,0.000155922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001320192,0.0002059095,0.01783412,0.0002278967,0.000003463109,0.00006248621,0.0007407919,0.0005084786,0.1172306,0.001953227,0.002540264,0.8585608],"study_design_scores_gemma":[0.001086917,0.00008587494,0.01302387,0.0001253198,0.0000653643,0.0001516952,0.0004245226,0.8619903,0.006082544,0.00084944,0.1157851,0.0003289566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02808085,0.003313963,0.9561628,0.004854637,0.00002315481,0.0006801044,0.000007519332,0.0003604164,0.006516547],"genre_scores_gemma":[0.5555643,0.0001225721,0.4421978,0.001634714,0.0001549745,0.00005421941,0.00001561038,0.00003690006,0.0002188778],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8614818,"threshold_uncertainty_score":0.6489297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04369012124895362,"score_gpt":0.3077325715934421,"score_spread":0.2640424503444885,"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."}}