{"id":"W4393864600","doi":"10.11603/mie.1996-1960.2023.3-4.14471","title":"РОЗРОБЛЕННЯ МОДЕЛІ МАШИННОГО НАВЧАННЯ ДЛЯ ДИФЕРЕНЦІЙНОЇ ДІАГНОСТИКИ ТРАНЗИТОРНИХ ВТРАТ СВІДОМОСТІ СИНКОПАЛЬНОГО ТА НЕСИНКОПАЛЬНОГО ПОХОДЖЕННЯ У ДІТЕЙ","year":2024,"lang":"uk","type":"article","venue":"Medical Informatics and Engineering","topic":"Epilepsy research and treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science","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","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.002067212,0.001304194,0.001626459,0.001030482,0.0003710105,0.0008382854,0.0006802547,0.001133926,0.002505373],"category_scores_gemma":[0.00115935,0.001071476,0.0005438331,0.001307439,0.0004784606,0.00083471,0.0007242822,0.00310476,0.001513728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005264289,"about_ca_system_score_gemma":0.001234973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002376468,"about_ca_topic_score_gemma":0.00003852159,"domain_scores_codex":[0.9911364,0.00008437502,0.002347847,0.0007600175,0.003143029,0.002528286],"domain_scores_gemma":[0.9938818,0.0009664386,0.0001776932,0.001042703,0.000222072,0.00370933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006983133,0.002878271,0.009138045,0.06528744,0.009414153,0.01537243,0.03180024,0.002040533,0.0008121894,0.04609499,0.1464096,0.6700538],"study_design_scores_gemma":[0.004863079,0.001547628,0.001842023,0.009298305,0.0008605667,0.001239015,0.002222475,0.8049117,0.0004186209,0.0002933892,0.1709493,0.001553962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6904949,0.1362897,0.06504641,0.02500538,0.01553709,0.005514738,0.0009004839,0.003854433,0.05735688],"genre_scores_gemma":[0.9609888,0.02379,0.005668681,0.001610003,0.003116441,0.0002454848,0.00048267,0.00033519,0.003762802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8028711,"threshold_uncertainty_score":0.999971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01117047701723338,"score_gpt":0.2736551110942241,"score_spread":0.2624846340769907,"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."}}