{"id":"W2806326686","doi":"10.1007/s41060-018-0130-1","title":"FACTORBASE: multi-relational structure learning with SQL all the way","year":2018,"lang":"en","type":"article","venue":"International Journal of Data Science and Analytics","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Statistical relational learning; Relational database; SQL; Relational model; Database design; Database model; Bayesian network; Relational database management system; Data definition language; Database; Machine learning; Artificial intelligence; Data mining","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009230549,0.00008003561,0.00008193051,0.0001452244,0.0002169941,0.000500072,0.002909906,0.00002329566,0.00001504828],"category_scores_gemma":[0.0002561168,0.00004443105,0.0000154307,0.00030924,0.0005324135,0.002399799,0.0004897666,0.0002634867,0.000002947136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003636873,"about_ca_system_score_gemma":0.0003733755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001166806,"about_ca_topic_score_gemma":0.00001910843,"domain_scores_codex":[0.9983018,0.00002556633,0.0002217464,0.0002202687,0.001087287,0.0001433479],"domain_scores_gemma":[0.997867,0.00008307526,0.0002446094,0.0002818842,0.001430143,0.00009327372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002076067,0.0004845835,0.07017756,0.00001906377,0.001226452,0.0003599051,0.01165924,0.01957573,0.03483419,0.3757946,0.008525337,0.4771357],"study_design_scores_gemma":[0.0003953224,0.0001989354,0.01015676,0.00006101766,0.00002418872,0.0004363911,0.0001382273,0.9749972,0.0006813369,0.001590153,0.01117599,0.0001444922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04811003,0.00005922109,0.9474202,0.003922069,0.0003322816,0.00002153882,0.00001905616,0.000009074186,0.0001065642],"genre_scores_gemma":[0.9458467,0.00004168498,0.05332601,0.0005080135,0.0002305224,7.498291e-8,0.000004420889,0.000002446663,0.00004017249],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9554214,"threshold_uncertainty_score":0.5407379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09284649269330404,"score_gpt":0.3453022686637123,"score_spread":0.2524557759704083,"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."}}