{"id":"W6997751733","doi":"","title":"ІВАН ФРАНКО І СТЕФАН КОВАЛІВ: ДО ІСТОРІЇ ВЗАЄМИН ТА СПІВРОБІТНИЦТВА","year":2018,"lang":"en","type":"article","venue":"Scientific periodicals of Ukraine","topic":"Scientific Research and Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Ukrainian; Population; Working class; Natural (archaeology); State (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","sts","insufficient_payload"],"consensus_categories":["sts","insufficient_payload"],"category_scores_codex":[0.003310568,0.0003102813,0.0004751734,0.0002458493,0.001538865,0.000435953,0.001430546,0.0001214178,0.02929103],"category_scores_gemma":[0.001264905,0.0002466799,0.0002312431,0.001918522,0.0121693,0.000424267,0.001207696,0.0002434923,0.006371253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001241617,"about_ca_system_score_gemma":0.0001165981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002238877,"about_ca_topic_score_gemma":0.0005026712,"domain_scores_codex":[0.994785,0.0001594201,0.000695032,0.001247121,0.001930443,0.00118304],"domain_scores_gemma":[0.997609,0.0001328526,0.0002029637,0.001290464,0.0001692513,0.0005954682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001377039,0.0007237335,0.02558116,0.00006312609,0.00008718081,0.00004859758,0.007320351,0.00002543213,0.2973839,0.0006973502,0.6022607,0.06567073],"study_design_scores_gemma":[0.001177,0.0006118251,0.07925744,0.00008731973,0.00003334731,0.00002602357,0.000980241,0.001798104,0.05257329,0.003257508,0.8594365,0.0007614021],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9218788,0.0003386267,0.0001971744,0.00176323,0.001840103,0.0004103176,0.0001196307,0.0001074006,0.0733447],"genre_scores_gemma":[0.9636526,0.00003449256,0.001583684,0.000187691,0.0002152551,0.00002952678,0.00002194929,0.0000217931,0.03425303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2571758,"threshold_uncertainty_score":0.9999986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01763892014654204,"score_gpt":0.2864592215562144,"score_spread":0.2688203014096724,"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."}}