{"id":"W7140021043","doi":"","title":"РІДКІ НЕЙРОННІ МЕРЕЖІ: ПРИНЦИП РОБОТИ ТА ОБЛАСТІ ЗАСТОСУВАННЯ","year":2024,"lang":"uk","type":"article","venue":"A.N.Beketov KNUME Digital Repository (National University of Kharkiv)","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Technische Universität Wien; Universität Wien; Institute for Catastrophic Loss Reduction","keywords":"Process (computing); Identification (biology); Product (mathematics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002156045,0.0005840371,0.0005876819,0.0006356457,0.0004185079,0.0002930294,0.0008923899,0.0006840438,0.0005303195],"category_scores_gemma":[0.0001129227,0.000747641,0.0005460964,0.0007397203,0.0007484759,0.002253993,0.0002913554,0.0008458055,0.0007640868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006096129,"about_ca_system_score_gemma":0.0008073433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001529078,"about_ca_topic_score_gemma":0.00003985772,"domain_scores_codex":[0.9969233,0.00006601217,0.0006129543,0.0008643189,0.000917107,0.0006163321],"domain_scores_gemma":[0.9982141,0.000456945,0.0001447652,0.0005252329,0.0004113152,0.0002476926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007569879,0.001197307,0.007820893,0.005063892,0.006405013,0.007088054,0.004770399,0.009185351,0.01033154,0.8030992,0.1100288,0.0342526],"study_design_scores_gemma":[0.00450034,0.001692901,0.03363321,0.004568675,0.001503815,0.00229742,0.01575053,0.05852529,0.008320109,0.1545637,0.7082921,0.006351919],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1554959,0.007283116,0.00183534,0.0007042037,0.002577943,0.0004634315,0.0008316297,0.001372428,0.8294361],"genre_scores_gemma":[0.9648383,0.0003907117,0.0002810094,0.00002284921,0.0002823961,0.000001540314,0.0002010269,0.0000649466,0.03391728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8093424,"threshold_uncertainty_score":0.9994975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006531272292883377,"score_gpt":0.1775173867789942,"score_spread":0.1709861144861108,"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."}}