{"meta":{"query_hash":"658ee0da500d","filters":{"venue":"2020 Intermountain Engineering, Technology and Computing (IETC)"},"cohort_total":1,"direct_labels_cover":0,"predictions_cover":1,"exported":1,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/658ee0da500d","api":"https://metacan.xera.ac/api/v1/cohort?venue=2020+Intermountain+Engineering%2C+Technology+and+Computing+%28IETC%29"},"results":[{"id":"W3103089735","doi":"10.1109/ietc47856.2020.9249147","title":"A Speech Emotion Recognition Solution-based on Support Vector Machine for Children with Autism Spectrum Disorder to Help Identify Human Emotions","year":2020,"lang":"en","type":"article","venue":"2020 Intermountain Engineering, Technology and Computing (IETC)","topic":"Autism Spectrum Disorder Research","field":"Neuroscience","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mel-frequency cepstrum; Python (programming language); Computer science; Support vector machine; Speech recognition; Feature extraction; Autism spectrum disorder; Artificial intelligence; Autism; Machine learning; Word error rate; Speech processing; Psychology","score_opus":0.013853413643112357,"score_gpt":0.2633662058215708,"score_spread":0.24951279217845843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3103089735","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13343483,0.00044766042,0.8412303,0.0009077742,0.00024391552,0.00043580536,0.0013923272,0.015257653,0.0066497144],"genre_scores_gemma":[0.61130685,0.0003391784,0.37343517,0.00029157722,0.00004686242,0.0006452442,0.0021806709,0.000313698,0.01144075],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997272,0.00004478959,0.000018107059,0.00008437755,0.00009128019,0.000034194767],"domain_scores_gemma":[0.99973565,0.00007287478,0.00002150381,0.00002126048,0.00012583457,0.0000228918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038400939,0.0006067048,0.00036525103,0.00040846324,0.00023585501,0.00042498505,0.0005977736,0.00048861944,0.003521278],"category_scores_gemma":[0.0012282067,0.00017587935,0.0005642413,0.00015798575,0.00011789558,0.0005106095,0.000564029,0.00058204617,0.0015540001],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036655416,0.00040622434,0.01370944,0.0002874733,0.00014970276,0.00061076833,0.00039968433,0.041561816,0.043969933,0.0025758364,0.016450519,0.879512],"study_design_scores_gemma":[0.000051100662,0.00034270593,0.0104669975,0.000058015685,0.000073907846,0.00059159234,0.00022538395,0.9383099,0.035486314,0.0027355792,0.011611778,0.000046655518],"about_ca_topic_score_codex":0.0030281418,"about_ca_topic_score_gemma":0.0038044022,"teacher_disagreement_score":0.003521278,"about_ca_system_score_codex":0.00035953108,"about_ca_system_score_gemma":0.00067635026,"threshold_uncertainty_score":0.011779785},"labels":[],"label_agreement":null}]}