{"id":"W2794925563","doi":"10.1155/2018/8039075","title":"A Sorting Statistic with Application in Neurological Magnetic Resonance Imaging of Autism","year":2018,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre; St. Francis Xavier University","funders":"National Institute of Neurological Disorders and Stroke; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Eunice Kennedy Shriver National Institute of Child Health and Human Development; St. Francis Xavier University; Canada Foundation for Innovation; Nova Scotia Research Innovation Trust","keywords":"Autism; Magnetic resonance imaging; Statistic; Sorting; Functional magnetic resonance imaging; Computer science; Medicine; Psychology; Neuroscience; Statistics; Psychiatry; Mathematics; Radiology","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":[0.0003343459,0.00008630441,0.0001960952,0.0001703541,0.0000467818,0.00000803931,0.0001089999,0.00002110339,0.00000261644],"category_scores_gemma":[0.002733349,0.0000727091,0.00002259784,0.0003286986,0.00006454015,0.0001171237,0.00002908603,0.0002699484,0.000001102939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004335697,"about_ca_system_score_gemma":0.00005726287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004042221,"about_ca_topic_score_gemma":0.00003738043,"domain_scores_codex":[0.9989381,0.00005645179,0.0003897602,0.0001618796,0.0002572869,0.0001965176],"domain_scores_gemma":[0.9982077,0.001331598,0.0002238802,0.00009364261,0.00009389358,0.0000492722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001113245,0.0002907002,0.556717,0.0007875758,0.00001136157,0.000781171,0.002709799,0.02078066,0.1790525,0.03221028,0.0002352333,0.2053105],"study_design_scores_gemma":[0.000837668,0.001699912,0.8880098,0.000369072,0.000008510673,0.0007567255,0.00007251781,0.1007825,0.004671957,0.001060803,0.001537357,0.0001931342],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9663686,0.00124993,0.02478878,0.007154434,0.0002358756,0.0001471177,0.000003649513,0.00002322406,0.000028412],"genre_scores_gemma":[0.9960842,0.00002521607,0.003378017,0.0004037757,0.00008907614,0.000005347486,6.514741e-8,0.00001173847,0.000002638821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3312928,"threshold_uncertainty_score":0.3272271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0169255996794884,"score_gpt":0.2660653703960567,"score_spread":0.2491397707165683,"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."}}