{"id":"W4392247853","doi":"10.32782/2524-0072/2024-59-87","title":"ЕФЕКТИВНИЙ ЕКОНОМІЧНИЙ РОЗВИТОК ПІДПРИЄМСТВА ЧЕРЕЗ ІНТЕЛЕКТУАЛЬНИЙ АНАЛІЗ ДАНИХ: ВИКОРИСТАННЯ AI ДЛЯ ПРОГНОЗУВАННЯ ТА ОПТИМІЗАЦІЇ СТРАТЕГІЙ БІЗНЕСУ","year":2024,"lang":"uk","type":"article","venue":"Економіка та суспільство","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mitel (Canada)","funders":"","keywords":"Computer 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":[0.002173729,0.003861248,0.00290877,0.002337381,0.001027565,0.004840763,0.003483485,0.003363873,0.01636383],"category_scores_gemma":[0.0004595972,0.004273895,0.002195228,0.004684709,0.00106692,0.006579291,0.0008350344,0.006802584,0.04308293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002172182,"about_ca_system_score_gemma":0.001858499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000433681,"about_ca_topic_score_gemma":0.000188304,"domain_scores_codex":[0.9822462,0.0004705595,0.005116881,0.003485988,0.003508415,0.005171976],"domain_scores_gemma":[0.9913179,0.001169036,0.0005559572,0.003881932,0.0007729553,0.00230217],"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":[0.0004094984,0.001464508,0.001816575,0.01282164,0.00503275,0.002484757,0.01088485,0.02884294,0.005412765,0.154469,0.6455915,0.1307693],"study_design_scores_gemma":[0.003152438,0.0006685829,0.001294315,0.004684485,0.001190843,0.0007851934,0.002205607,0.03789888,0.009671555,0.01099635,0.9211923,0.0062594],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04546048,0.03314248,0.02754139,0.01325476,0.04430202,0.005510328,0.004886214,0.01407432,0.811828],"genre_scores_gemma":[0.9501476,0.002811005,0.00191257,0.002052604,0.006163327,0.000661006,0.001064586,0.001749407,0.03343793],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9046871,"threshold_uncertainty_score":0.99793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.014468716617096,"score_gpt":0.2425169398591161,"score_spread":0.2280482232420201,"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."}}