{"id":"W2952472276","doi":"10.1101/491720","title":"BOLD signal variability and complexity in children and adolescents with and without autism spectrum disorder","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autism spectrum disorder; Psychology; Sample entropy; Categorical variable; Connectome; Correlation; Resting state fMRI; Developmental psychology; Autism; Audiology; Neuroscience; Cognitive psychology; Pattern recognition (psychology); Functional connectivity; Machine learning; Computer science; Medicine; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000244597,0.000194054,0.0001938341,0.0007488567,0.000133669,0.000272062,0.000108988,0.0002744986,0.0006514843],"category_scores_gemma":[0.001134044,0.0001273837,0.00012606,0.0002610484,0.000285276,0.0001983352,0.0002778198,0.0003017886,0.00007001236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000179819,"about_ca_system_score_gemma":0.00009566427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001287738,"about_ca_topic_score_gemma":0.002440551,"domain_scores_codex":[0.9998399,0.00002542248,0.00001471078,0.00005441958,0.00004166298,0.00002387226],"domain_scores_gemma":[0.9994253,0.0001395649,0.0002894501,0.00002335077,0.0000418233,0.00008056818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005075288,0.00007164026,0.9516898,0.0000499641,0.00009890478,0.0006537734,0.0008052274,0.0001843636,0.03511253,0.0001294255,0.0001931145,0.0105037],"study_design_scores_gemma":[0.000002057639,0.00004145605,0.9989942,0.000001617444,0.000004323874,0.0003969623,0.00009323439,0.00009173484,0.0003129542,0.00002148696,0.00003815258,0.000001912635],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995277,0.00005765786,0.0001179629,0.00001576047,0.000001205337,0.000003171809,0.00009655359,0.000005189336,0.0001748637],"genre_scores_gemma":[0.9993192,0.00005958329,0.0003676058,0.00001640259,0.000004027951,0.0000119671,0.0001383942,0.000004622324,0.0000781268],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001287738,"threshold_uncertainty_score":0.002560496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01979662704561854,"score_gpt":0.2240267613680395,"score_spread":0.2042301343224209,"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."}}