{"id":"W2038277297","doi":"10.1118/1.3583814","title":"Spin‐history artifact during functional MRI: Potential for adaptive correction","year":2011,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; Health Sciences Centre; Profound Medical (Canada); University of Toronto; Heart and Stroke Foundation; Sunnybrook Health Science Centre","funders":"","keywords":"Artifact (error); Imaging phantom; Computer science; Computer vision; Artificial intelligence; SIGNAL (programming language); Functional magnetic resonance imaging; Amplitude; Magnetic resonance imaging; Physics; Nuclear magnetic resonance; Optics; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000601365,0.00009286146,0.000140389,0.00002191226,0.00007802986,0.00000131614,0.00004602643,0.00008580048,0.0005788353],"category_scores_gemma":[0.0000417259,0.00008418265,0.000106561,0.00005773654,0.0001076611,0.00006035958,0.00002212229,0.0001587968,0.00003639018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001628125,"about_ca_system_score_gemma":0.0001160727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001474758,"about_ca_topic_score_gemma":0.000001625226,"domain_scores_codex":[0.9992279,0.000005715649,0.0001514674,0.0001975214,0.0002589177,0.0001584418],"domain_scores_gemma":[0.9995181,0.00001965737,0.00006009175,0.0001517784,0.00009013472,0.0001602587],"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":[0.005907095,0.006676713,0.001831167,0.000374234,0.0003396224,0.0001431601,0.001792429,0.0004001076,0.03766775,0.056721,0.3036112,0.5845355],"study_design_scores_gemma":[0.01259915,0.004153242,0.1039289,0.000891209,0.00109352,0.0005998168,0.0005736591,0.07533796,0.4771359,0.122523,0.1991661,0.001997568],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007798037,0.00004018011,0.9869448,0.0001964465,0.0005347937,0.0003558845,0.000003915969,0.0001727218,0.003953197],"genre_scores_gemma":[0.9838821,0.0000318876,0.01137056,0.0004423774,0.001299791,0.0002576727,0.00005462018,0.00002883679,0.002632128],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9760841,"threshold_uncertainty_score":0.6337841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04705663853597609,"score_gpt":0.2859670713820066,"score_spread":0.2389104328460305,"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."}}