{"id":"W4401061165","doi":"10.31891/2307-5732-2024-335-17","title":"АНАЛІЗ ВИМОГ ДО ПОЗИЦІЙ DATA ANALYST ТА DATA SCIENTIST НА РИНКУ ПРАЦІ","year":2024,"lang":"en","type":"article","venue":"Herald of Khmelnytskyi National University Technical sciences","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03659519,0.0006652986,0.0009574695,0.006102459,0.002346175,0.008880588,0.001012511,0.00111258,0.01601696],"category_scores_gemma":[0.0697595,0.001183801,0.0008088058,0.01041958,0.00462281,0.004885253,0.00368625,0.002988485,0.008609795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002705124,"about_ca_system_score_gemma":0.01005221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002363701,"about_ca_topic_score_gemma":0.002146579,"domain_scores_codex":[0.9655018,0.01492793,0.003483038,0.003629661,0.01146847,0.0009890188],"domain_scores_gemma":[0.9269644,0.04057037,0.005061917,0.01000624,0.01527608,0.002120885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003704454,0.0001381052,0.01703778,0.002347931,0.000119605,0.0008191001,0.02091352,0.001556425,0.00903727,0.3052374,0.02304072,0.6193818],"study_design_scores_gemma":[0.00009727322,0.0002131663,0.02364006,0.0013469,0.0001329672,0.0009550962,0.01231368,0.003981022,0.01360563,0.1591377,0.7843745,0.0002019927],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07008944,0.009015036,0.7412661,0.01885589,0.002242159,0.002663941,0.006859743,0.002149516,0.1468581],"genre_scores_gemma":[0.4060808,0.007530904,0.5446893,0.001483934,0.001050406,0.005017331,0.002925446,0.001284397,0.0299375],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03659519,"threshold_uncertainty_score":0.1935361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2326414372031586,"score_gpt":0.3609673279733985,"score_spread":0.1283258907702399,"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."}}