{"id":"W4321514107","doi":"10.51788/tsul.jurisprudence.1.3./amku8027","title":"Strategies and prospects for the development of artificial intelligence in the world and in the Republic of Uzbekistan: a comparative analysis","year":2021,"lang":"en","type":"article","venue":"jurisprudence","topic":"Language Acquisition and Education","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"China; Artificial intelligence; Plan (archaeology); Soviet union; European union; Political science; Marketing and artificial intelligence; Computer science; Business; Law; International trade; Intelligent decision support system; Politics; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001766761,0.0002559403,0.0001701684,0.003672671,0.006111397,0.009501501,0.0003894177,0.0009329906,0.00260854],"category_scores_gemma":[0.001736104,0.0001052634,0.0001872105,0.005451291,0.004912242,0.003332526,0.001464836,0.0007010301,0.0001901367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01110736,"about_ca_system_score_gemma":0.007707644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02567417,"about_ca_topic_score_gemma":0.06563023,"domain_scores_codex":[0.9984114,0.0006673997,0.00005812894,0.00007874196,0.0001966779,0.0005876607],"domain_scores_gemma":[0.9992873,0.000213522,0.0001318126,0.00002198755,0.0002068244,0.0001384935],"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":[0.00005878412,0.0000585603,0.01266509,0.000131893,0.00002036182,0.0004767205,0.01591387,0.000305968,0.0003009924,0.9181081,0.001824989,0.05013468],"study_design_scores_gemma":[0.00005119573,0.0002487444,0.129126,0.001181021,0.00009988426,0.00104363,0.2719415,0.001355956,0.001466601,0.1198727,0.4735302,0.00008253739],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.492958,0.02285484,0.001660597,0.01102102,0.000123597,0.00005508035,0.00008128742,0.00002438332,0.4712212],"genre_scores_gemma":[0.9921159,0.003603993,0.0008110184,0.0001711822,0.000007654243,0.00001361284,0.00002066728,0.000003959835,0.003251981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02567417,"threshold_uncertainty_score":0.08058989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07258917906769367,"score_gpt":0.4140250140252632,"score_spread":0.3414358349575695,"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."}}