{"id":"W3008396432","doi":"10.1145/3318464.3380602","title":"Facilitating SQL Query Composition and Analysis","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Spatial query; Sargable; Query by Example; SQL; Query optimization; Web query classification; Class (philosophy); Query language; Query expansion; Web search query; Workload; Data mining; Database; Information retrieval; Artificial intelligence; Search engine; Operating system","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":[0.0001346238,0.0001676526,0.0003601697,0.0001250877,0.00009031294,0.0001233251,0.0002367928,0.00007187254,0.00001082969],"category_scores_gemma":[0.00002777736,0.0001498097,0.0001093347,0.000305887,0.00003351303,0.0002929896,0.001562355,0.0002145165,0.00001708826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002154465,"about_ca_system_score_gemma":0.00003266098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002622007,"about_ca_topic_score_gemma":0.0000367113,"domain_scores_codex":[0.9987365,0.00007398114,0.0002666892,0.0006159375,0.0001770217,0.0001298709],"domain_scores_gemma":[0.9990712,0.0001156414,0.0001312548,0.0005337594,0.00005641678,0.0000917137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001524122,0.00006113031,0.003577359,0.001248514,0.001821126,0.0001094295,0.008850148,0.01377256,0.004317441,0.9019755,0.001686687,0.06256485],"study_design_scores_gemma":[0.0002351914,0.00005194628,0.004129598,0.0001694831,0.0002865747,0.00001326012,0.0007072929,0.9760908,0.0008899161,0.007755253,0.008650652,0.001020007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004888747,0.0002612002,0.9923879,0.0006571902,0.0001425903,0.0001279195,0.0001537492,0.0002299094,0.001150782],"genre_scores_gemma":[0.2061668,0.00003071312,0.7930259,0.0002638427,0.00005218396,0.00002287693,0.000315708,0.000005452129,0.0001165466],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9623182,"threshold_uncertainty_score":0.6109061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02791884469615542,"score_gpt":0.2768290117648426,"score_spread":0.2489101670686872,"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."}}