{"id":"W4322500097","doi":"10.36713/epra12492","title":"IMPORTANCE OF DATA INTEGRITY IN PHARMACEUTICAL INDUSTRY","year":2023,"lang":"en","type":"article","venue":"EPRA International Journal of Economics Business and Management Studies","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Data integrity; Metadata; Scientific integrity; Business; Computer science; Data science; Data retention; Personal Integrity; Computer security; Risk analysis (engineering); Engineering; World Wide Web","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":[],"consensus_categories":[],"category_scores_codex":[0.006303918,0.00009324024,0.0003271012,0.0006500134,0.00003063705,0.0001038879,0.001431769,0.00003375345,0.00004060708],"category_scores_gemma":[0.001126994,0.00007328548,0.00003621218,0.0004027148,0.0001663842,0.001095357,0.002396442,0.0001917181,0.00001169455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004596456,"about_ca_system_score_gemma":0.00002389281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001480159,"about_ca_topic_score_gemma":0.0001292803,"domain_scores_codex":[0.9981557,0.00006345324,0.001053679,0.000263372,0.0003465257,0.0001172341],"domain_scores_gemma":[0.9981605,0.0004364717,0.0006275285,0.0003218429,0.0004173618,0.00003627001],"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":[0.0003848492,0.0004217063,0.1776478,0.0001906949,0.002288802,0.0003975075,0.0007716809,0.002765462,0.000006882563,0.2424107,0.1024716,0.4702422],"study_design_scores_gemma":[0.001677322,0.00003144056,0.5745782,0.0002447204,0.00008584563,0.00001923027,0.01272747,0.003680883,0.00001894671,0.1027054,0.3039917,0.0002387538],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9665669,0.0005936521,0.0009886917,0.0275308,0.001764836,0.0001491595,0.0001169655,0.000007601578,0.002281358],"genre_scores_gemma":[0.9650613,0.03194415,0.001477053,0.0007659271,0.0001712289,0.000004215682,0.00002319009,0.000006858197,0.0005460867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4700035,"threshold_uncertainty_score":0.2988495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.532101745288129,"score_gpt":0.5177898149369912,"score_spread":0.01431193035113787,"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."}}