{"id":"W4396640278","doi":"10.52058/2786-6025-2024-4(32)-1192-1205","title":"МЕТОД ІНТЕРПОЛЯЦІЇ ДЛЯ ПРОГНОЗУВАННЯ МЕТРИК ВИКОРИСТАННЯ ХМАРНИХ ОБЧИСЛЕНЬ В СТАТИСТИЧНОМУ НАВЧАННІ","year":2024,"lang":"uk","type":"article","venue":"Наука і техніка сьогодні","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Political science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.002663935,0.002880891,0.002868468,0.001480706,0.0008130617,0.001818306,0.002946856,0.002126872,0.01317742],"category_scores_gemma":[0.001745265,0.002864601,0.001537387,0.002607276,0.001238186,0.002480942,0.001744369,0.003995774,0.01780856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001638951,"about_ca_system_score_gemma":0.001696742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003625474,"about_ca_topic_score_gemma":0.0002590933,"domain_scores_codex":[0.9859949,0.0007485941,0.003513373,0.003388945,0.002494349,0.003859822],"domain_scores_gemma":[0.9895244,0.003205311,0.0009288916,0.004374926,0.0006652664,0.001301221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000243378,0.001125038,0.00019569,0.004942094,0.001354834,0.001855651,0.00486972,0.00009956274,0.01027372,0.7171117,0.1889242,0.06900438],"study_design_scores_gemma":[0.001263938,0.001139329,0.0001510128,0.004178192,0.001276255,0.0004713115,0.001290824,0.006654398,0.02242824,0.5167702,0.4403754,0.004000883],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0243504,0.06011949,0.2582785,0.01240088,0.0163868,0.0102381,0.00280761,0.02548075,0.5899374],"genre_scores_gemma":[0.8334154,0.006496428,0.05719243,0.002209229,0.004460061,0.001078555,0.000443814,0.001878073,0.09282602],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.809065,"threshold_uncertainty_score":0.9992179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2156556062908056,"score_gpt":0.4754551186532032,"score_spread":0.2597995123623975,"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."}}