{"id":"W2970397632","doi":"10.14778/3352063.3352102","title":"Making an RDBMS data scientist friendly","year":2019,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Python (programming language); Relational database management system; Database; Implementation; Relational database; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005192709,0.0001318571,0.0001704057,0.00006079618,0.0001518843,0.000120929,0.002735211,0.00002461791,0.00001632749],"category_scores_gemma":[0.00004972785,0.00008821317,0.00003951248,0.000366611,0.000073441,0.002277254,0.002720603,0.00009434558,0.00003182367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003753192,"about_ca_system_score_gemma":0.00003699462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004447783,"about_ca_topic_score_gemma":0.000006361337,"domain_scores_codex":[0.9984115,0.000006624878,0.0002584587,0.0005579578,0.0004909601,0.0002744508],"domain_scores_gemma":[0.9985482,0.00001627316,0.0002637542,0.0009934909,0.0001234971,0.00005482234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001047003,0.0001014905,0.00465114,0.0001551282,0.0000230785,7.827186e-7,0.0007564136,0.00002192056,0.06998489,0.9117833,0.003202193,0.009309152],"study_design_scores_gemma":[0.001736448,0.0005832731,0.0100351,0.001040669,0.00004572979,0.0001574548,0.002099119,0.03656138,0.1596383,0.009006774,0.7779661,0.001129727],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7517179,0.0008241179,0.170919,0.002535334,0.00758524,0.003299675,0.0003865153,0.000671428,0.0620608],"genre_scores_gemma":[0.910773,0.000005971976,0.08838612,0.0001195761,0.00007322623,0.00001292123,0.000005900171,0.00001079044,0.0006125304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9027765,"threshold_uncertainty_score":0.5082747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04182728214053458,"score_gpt":0.2994120196958087,"score_spread":0.2575847375552741,"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."}}