{"id":"W4392348262","doi":"10.18280/ts.410102","title":"Autism Classification and Identification of Significant Brain Lobe Using Cepstral Coefficients","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Autism; Temporal lobe; Cepstrum; Psychology; Mel-frequency cepstrum; Audiology; Speech recognition; Pattern recognition (psychology); Artificial intelligence; Cognitive psychology; Computer science; Neuroscience; Developmental psychology; Epilepsy; Feature extraction; Medicine; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004873624,0.0005250548,0.0002938398,0.002369837,0.000169029,0.0003997634,0.0002170244,0.0004201668,0.001123029],"category_scores_gemma":[0.002189731,0.0001175098,0.0004692701,0.0007259263,0.0001721948,0.0003426247,0.0003205207,0.0003130808,0.0004332937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002132014,"about_ca_system_score_gemma":0.0002497456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007385571,"about_ca_topic_score_gemma":0.006055765,"domain_scores_codex":[0.9997273,0.00004177956,0.00002826456,0.00006547572,0.00008099034,0.00005617638],"domain_scores_gemma":[0.9994071,0.0002575016,0.00007881514,0.00003493684,0.0001885839,0.00003308006],"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":[0.0009894661,0.0002138432,0.123899,0.0003055307,0.0001871588,0.001743176,0.0007912799,0.01958913,0.2068958,0.00104042,0.002820301,0.6415249],"study_design_scores_gemma":[0.00003875018,0.0003977486,0.5498701,0.0001039269,0.0001755893,0.002535024,0.001094089,0.4013775,0.04087105,0.0007750767,0.00267138,0.00008975444],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9192123,0.0006645251,0.07695737,0.00009102921,0.00004839687,0.00009385261,0.000841405,0.0003947174,0.001696363],"genre_scores_gemma":[0.9641962,0.0002774255,0.03412348,0.0000107323,0.00001156593,0.0000548439,0.0008112526,0.00002038195,0.0004941839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007385571,"threshold_uncertainty_score":0.01468521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05269304614369665,"score_gpt":0.3047149250511957,"score_spread":0.2520218789074991,"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."}}