{"id":"W4386765834","doi":"10.32620/reks.2017.3.06","title":"МЕТОД НАВЧАННЯ БЕЗ ВЧИТЕЛЯ ІЄРАРХІЧНОГО ЕКСТРАКТОРА ВІЗУАЛЬНИХ ОЗНАК НА ОСНОВІ МОДИФІКАЦІЇ НЕЙРОННОГО ГАЗУ","year":2019,"lang":"en","type":"article","venue":"RADIOELECTRONIC AND COMPUTER SYSTEMS","topic":"Enterprise Management and Information Systems","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Catastrophic Loss Reduction","keywords":"Computer science; Pattern recognition (psychology); Artificial intelligence; Feature (linguistics); Coding (social sciences); Binary number; Artificial neural network; Binary code; Partition (number theory); Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007264097,0.0005556467,0.0004740992,0.001691196,0.0009045355,0.002434132,0.0005795139,0.0007756337,0.03464415],"category_scores_gemma":[0.002311424,0.0006008499,0.0004694596,0.001796046,0.001249444,0.00163187,0.001335767,0.001524467,0.01412681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008957778,"about_ca_system_score_gemma":0.001741737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002578862,"about_ca_topic_score_gemma":0.002800134,"domain_scores_codex":[0.9992794,0.00009991782,0.0000478428,0.000142313,0.0003584948,0.00007204845],"domain_scores_gemma":[0.9993799,0.0001463504,0.00006851672,0.0001657595,0.0001892773,0.00005014435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002829445,0.0001138337,0.001221358,0.0005405005,0.00004356825,0.0009799188,0.0006455943,0.00692201,0.04612642,0.348805,0.01692555,0.5773932],"study_design_scores_gemma":[0.00005573677,0.0001977835,0.003867329,0.0003345628,0.00008172572,0.001899346,0.0006216727,0.01381851,0.04006112,0.1444209,0.7944643,0.0001770456],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04351039,0.01419497,0.5526274,0.002246996,0.002992385,0.0003457601,0.001642612,0.001862205,0.3805772],"genre_scores_gemma":[0.4483199,0.01863152,0.3869754,0.0005316791,0.0008553189,0.0006860768,0.001538777,0.001172655,0.1412886],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03464415,"threshold_uncertainty_score":0.1158962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004295954374604241,"score_gpt":0.1622266809164084,"score_spread":0.1579307265418041,"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."}}