{"id":"W4234432664","doi":"10.32920/ryerson.14668077","title":"A mapreduce relational-database index-selection tool","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Selection (genetic algorithm); Index (typography); Index selection; Task (project management); Set (abstract data type); Data mining; Process (computing); Relational database; Big data; Database; Systems engineering; Engineering; Artificial intelligence","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":[0.001203994,0.0009185711,0.0006835252,0.001381957,0.0009363943,0.001455141,0.002743578,0.0005865054,0.005172526],"category_scores_gemma":[0.00312354,0.0006991829,0.0007271944,0.001598671,0.0003174894,0.001602798,0.001255626,0.001045368,0.003721951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005771726,"about_ca_system_score_gemma":0.001541571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003817799,"about_ca_topic_score_gemma":0.004567807,"domain_scores_codex":[0.998911,0.0001189893,0.00009098258,0.000229356,0.0005624632,0.00008726941],"domain_scores_gemma":[0.9990615,0.0002307119,0.00005334738,0.0002895523,0.000270823,0.00009401455],"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":[0.0006419026,0.000748823,0.004605872,0.0005725782,0.0002389663,0.0006720478,0.0003500597,0.03554428,0.04724123,0.01397517,0.2324423,0.6629668],"study_design_scores_gemma":[0.0002452811,0.0002705732,0.002908366,0.00005825975,0.000062895,0.0007109515,0.0002190335,0.7940688,0.06502888,0.02093055,0.1153495,0.0001469631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02185691,0.0004772553,0.7260368,0.00058532,0.0002349137,0.0006520516,0.005188164,0.2347328,0.01023575],"genre_scores_gemma":[0.1291335,0.0003008165,0.8446684,0.0003587995,0.00007124789,0.0003811725,0.01173793,0.00503223,0.008315816],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005172526,"threshold_uncertainty_score":0.01730382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02981339338773392,"score_gpt":0.2702355443306068,"score_spread":0.2404221509428728,"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."}}