{"id":"W2578082361","doi":"","title":"Using Semantic Web Technologies for Explaining and Predicting Abnormal Expenses.","year":2016,"lang":"en","type":"article","venue":"International Semantic Web Conference","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Semantic Web; Information retrieval","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001144818,0.0007236741,0.0003874115,0.00744716,0.0004096333,0.002176522,0.000545444,0.001073948,0.002378084],"category_scores_gemma":[0.007483835,0.0001952477,0.0007351846,0.005133585,0.0002992763,0.003617391,0.0005875435,0.00072251,0.0008840734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00084316,"about_ca_system_score_gemma":0.001057983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006558517,"about_ca_topic_score_gemma":0.009136599,"domain_scores_codex":[0.9991187,0.0002167115,0.0001202109,0.0001233373,0.0003483391,0.00007263891],"domain_scores_gemma":[0.9970161,0.001537953,0.0006224937,0.0001786768,0.0005556121,0.00008919906],"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":[0.0009339554,0.001158324,0.3985201,0.0009482352,0.0007840325,0.002439369,0.0007201132,0.04300145,0.005648504,0.04338936,0.03274876,0.4697077],"study_design_scores_gemma":[0.000136475,0.0002795893,0.1440857,0.0006925204,0.0007483508,0.001592899,0.00318073,0.6246901,0.01505828,0.1588091,0.05058084,0.0001454446],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7061123,0.006762251,0.2049939,0.005557497,0.0005612059,0.0005723386,0.02403567,0.004351231,0.04705365],"genre_scores_gemma":[0.9300526,0.001828147,0.05710635,0.0001752423,0.000130343,0.0001114132,0.008630001,0.00009646652,0.001869308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00744716,"threshold_uncertainty_score":0.01304066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05009066489494174,"score_gpt":0.2993295164954154,"score_spread":0.2492388516004737,"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."}}