{"id":"W1976455825","doi":"10.4236/jcc.2014.25001","title":"Knowledge Discovery in Data: A Case Study","year":2014,"lang":"en","type":"article","venue":"Journal of Computer and Communications","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Canadian Natural Resources; Alberta Health Services","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Online analytical processing; Data warehouse; Computer science; Knowledge extraction; Data science; Domain (mathematical analysis); Data mining; Domain knowledge; Data collection; Database; Software engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006621654,0.00006800398,0.0001501957,0.0001973999,0.000117225,0.0003302976,0.001058712,0.00001789969,0.000004402335],"category_scores_gemma":[0.00003565397,0.00005374572,0.00001875483,0.0002671131,0.0000519526,0.002142085,0.001411377,0.0001722421,0.000007437591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005806431,"about_ca_system_score_gemma":0.00001570499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000239877,"about_ca_topic_score_gemma":0.001344003,"domain_scores_codex":[0.9994146,0.00003659554,0.0003145387,0.00008693881,0.00007361308,0.00007369847],"domain_scores_gemma":[0.9986244,0.0001289089,0.0001827946,0.0009249284,0.0001310111,0.000007920036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006424744,0.005711903,0.2604812,0.0002084164,0.0001564278,0.0003627683,0.00219309,0.0001608648,0.00002435328,0.04088961,0.02015262,0.6695945],"study_design_scores_gemma":[0.002205938,0.0001197626,0.1091878,0.0003856888,0.0002376903,0.002460726,0.003730227,0.2928285,0.000001687449,0.005011365,0.583338,0.0004927415],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9140614,0.001226507,0.0790986,0.002130645,0.0005151727,0.000181676,0.000003581817,0.00001714536,0.002765252],"genre_scores_gemma":[0.9978447,0.0000749238,0.001367186,0.000199778,0.0004807523,0.000001257835,0.000007344573,0.000004931553,0.00001910228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6691018,"threshold_uncertainty_score":0.3185067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.170334607172913,"score_gpt":0.3678639802994504,"score_spread":0.1975293731265374,"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."}}