{"id":"W4387504799","doi":"10.51889/2022-2.1728-7901.13","title":"МАШИНАЛЫҚ ОҚЫТУДЫ ПАЙДАЛАНЫП СӨЙЛЕУ ЭМОЦИЯЛАРЫН ТАНУ","year":2023,"lang":"ru","type":"article","venue":"Habaršy. Fizika-matematikalyķ ġylymdary seriâsy","topic":"Computational Physics and Python Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Disgust; Python (programming language); Spectrogram; Mel-frequency cepstrum; Speech recognition; Classifier (UML); Artificial intelligence; Artificial neural network; Emotion recognition; Anger; Feature extraction; Deep learning; Pattern recognition (psychology); Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.001744459,0.001492098,0.001670779,0.001021439,0.001652751,0.001833145,0.003866294,0.0005351973,0.001441684],"category_scores_gemma":[0.0001914495,0.001593075,0.0009351952,0.006647851,0.0004243794,0.002168577,0.00240374,0.001044942,0.03115768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000303994,"about_ca_system_score_gemma":0.001026095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003304407,"about_ca_topic_score_gemma":0.00005085551,"domain_scores_codex":[0.9897433,0.00059856,0.002464417,0.002671846,0.002288142,0.002233752],"domain_scores_gemma":[0.9918522,0.001377549,0.001155563,0.003739284,0.0008328632,0.001042514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009593253,0.001533755,0.0009756421,0.00310233,0.001032347,0.000635198,0.01015213,0.01659851,0.005806175,0.5651357,0.3435802,0.05135211],"study_design_scores_gemma":[0.002784099,0.0005682235,0.03124365,0.001528777,0.0003471617,0.0002872545,0.001085222,0.5670632,0.002845677,0.1752128,0.2129716,0.004062413],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2588931,0.00820288,0.418165,0.09366777,0.02420752,0.01523463,0.004970118,0.01720328,0.1594557],"genre_scores_gemma":[0.949463,0.0009213549,0.01736538,0.003260025,0.002193503,0.0008795076,0.001704188,0.0004365996,0.02377651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6905698,"threshold_uncertainty_score":0.9997828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02636606583204965,"score_gpt":0.2781414735979528,"score_spread":0.2517754077659031,"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."}}