{"id":"W7028109712","doi":"","title":"Diseño de un Datamart de aseguramiento de la calidad. Caso: Movistar Perú","year":2019,"lang":"es","type":"dissertation","venue":"Cybertesis (National University of San Marcos)","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cover (algebra); Field (mathematics); Quarter (Canadian coin)","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001297933,0.0004686766,0.0005422183,0.0006793161,0.0005271985,0.0003868769,0.001282419,0.0005018075,0.003351836],"category_scores_gemma":[0.0007750496,0.0005892775,0.000302774,0.0006764924,0.0003245204,0.001486809,0.0003503913,0.0004307348,0.000415451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006196743,"about_ca_system_score_gemma":0.000795125,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01691502,"about_ca_topic_score_gemma":0.001075311,"domain_scores_codex":[0.9972909,0.0001485418,0.0003929478,0.0006881665,0.0008447234,0.0006347304],"domain_scores_gemma":[0.9971933,0.0006087623,0.0006926031,0.000448803,0.0009644292,0.00009209105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002028635,0.002808716,0.6461357,0.009308673,0.001645291,0.0004829626,0.002602886,0.001419738,0.01505829,0.1324102,0.1527122,0.03338671],"study_design_scores_gemma":[0.001060587,0.00002302902,0.7364095,0.001114293,0.0009795651,0.00002091763,0.005073905,0.01560132,0.0007589718,0.002356136,0.2354018,0.001199966],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9425497,0.000483732,0.004187207,0.0008720503,0.000471774,0.0005606742,0.001546765,0.0001042256,0.04922386],"genre_scores_gemma":[0.9800813,0.0004660924,0.0009891966,0.0004086749,0.0003487855,0.000001849001,0.009327593,0.00005816101,0.00831835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.130054,"threshold_uncertainty_score":0.9996558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01921181700969287,"score_gpt":0.2562590291255595,"score_spread":0.2370472121158667,"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."}}