{"id":"W3114020188","doi":"10.21203/rs.3.rs-120600/v1","title":"fMRI-Based Machine Learning Analysis of Neural Substrates of Pediatric Anxiety: Temporal Pole and Emotional Face-Responses.","year":2020,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Anxiety; Psychology; Neurocognitive; Amygdala; Context (archaeology); Prefrontal cortex; Cognition; Cognitive psychology; Ventrolateral prefrontal cortex; Facial expression; Developmental psychology; Neuroscience; Communication; Psychiatry","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":[0.0007544801,0.0002885129,0.0001558646,0.0003732409,0.00006682732,0.00024326,0.0001671687,0.0001777246,0.0008433488],"category_scores_gemma":[0.002256056,0.00007689008,0.0002016126,0.0001913125,0.0001364885,0.0001486564,0.00008889517,0.0002392836,0.00009749138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001810891,"about_ca_system_score_gemma":0.0001688275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00134003,"about_ca_topic_score_gemma":0.002644201,"domain_scores_codex":[0.99988,0.00006936613,0.000003021023,0.00001930901,0.00001644852,0.00001186881],"domain_scores_gemma":[0.9994718,0.0003919674,0.00006955393,0.00002247408,0.00002541663,0.00001876646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001602961,0.0004670781,0.2378439,0.0003554685,0.0005496455,0.0005539987,0.00034799,0.06770435,0.3348993,0.002323803,0.001808897,0.3515426],"study_design_scores_gemma":[0.00002926402,0.0003856841,0.4140694,0.00002877696,0.000126307,0.0007803787,0.0001253475,0.5527599,0.02772688,0.003230059,0.0007136138,0.00002442101],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.922366,0.0005569542,0.07431928,0.0003194164,0.00001468108,0.00006401776,0.000463353,0.000243012,0.001653179],"genre_scores_gemma":[0.9880797,0.00008593864,0.01144327,0.00001493341,0.000008817506,0.00001909978,0.000137428,0.000007370429,0.0002034108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00134003,"threshold_uncertainty_score":0.003990114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1186246619010855,"score_gpt":0.4323437961060193,"score_spread":0.3137191342049337,"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."}}