{"id":"W2026457574","doi":"10.1006/nimg.2002.1076","title":"Motion Artifact in Magnetic Resonance Imaging: Implications for Automated Analysis","year":2002,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":146,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Artifact (error); Magnetic resonance imaging; Artificial intelligence; Computer science; Computer vision; Image quality; Reliability (semiconductor); Medicine; Radiology; Image (mathematics); Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.004604042,0.00064442,0.000968338,0.001823314,0.0007007329,0.003427766,0.001628406,0.001771192,0.001973098],"category_scores_gemma":[0.04364853,0.0004199644,0.0006114425,0.001847839,0.001442875,0.002385265,0.000910537,0.001162167,0.001088229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006398628,"about_ca_system_score_gemma":0.001327259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002623449,"about_ca_topic_score_gemma":0.004132737,"domain_scores_codex":[0.9976245,0.000912591,0.000199177,0.000282316,0.0009047416,0.00007665854],"domain_scores_gemma":[0.9748279,0.0174213,0.001577815,0.002776454,0.003145963,0.0002505582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005126641,0.0001680087,0.01302894,0.001178983,0.000294084,0.000466774,0.0004732817,0.02494095,0.06272646,0.03681108,0.008941811,0.850457],"study_design_scores_gemma":[0.0002061205,0.0003971368,0.05769577,0.0005194119,0.0005066883,0.006177006,0.0005156668,0.6242691,0.09085515,0.1836798,0.034938,0.0002402755],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02589148,0.005857018,0.9617844,0.002487987,0.0002966979,0.0001089519,0.0002126685,0.001243691,0.002117221],"genre_scores_gemma":[0.1994515,0.005639952,0.7900963,0.001015304,0.0005383892,0.0001203918,0.0003070166,0.0007414449,0.002089541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004604042,"threshold_uncertainty_score":0.0243488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03681904120243631,"score_gpt":0.3370215746566727,"score_spread":0.3002025334542364,"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."}}