{"id":"W2934771417","doi":"10.5441/002/edbt.2019.26","title":"A Six-dimensional Analysis of In-memory Aggregation","year":2019,"lang":"en","type":"article","venue":"Movebank","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science","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":[0.001643694,0.0007338315,0.0008170335,0.002564008,0.001291427,0.003700593,0.0013116,0.0005746334,0.008549834],"category_scores_gemma":[0.009414635,0.0003074182,0.0007673205,0.003308092,0.0008295536,0.003567658,0.001757252,0.001537203,0.001193752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001162173,"about_ca_system_score_gemma":0.0008263269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002882153,"about_ca_topic_score_gemma":0.001681688,"domain_scores_codex":[0.9982343,0.0003947529,0.00009987425,0.0001754857,0.0007940319,0.000301559],"domain_scores_gemma":[0.9957093,0.001548296,0.0003315443,0.001048005,0.001124867,0.0002380041],"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":[0.001141539,0.0006628597,0.01608081,0.000281939,0.0001648429,0.0005011723,0.0009225814,0.1328024,0.01397342,0.5823514,0.03218118,0.2189359],"study_design_scores_gemma":[0.00001702148,0.00007117748,0.004400935,0.00001953105,0.00004408347,0.0001729525,0.0002187023,0.8979283,0.005283276,0.08511417,0.006690576,0.00003923973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3177466,0.003110453,0.6397117,0.002252963,0.000486909,0.0001370309,0.002338111,0.003365943,0.03085032],"genre_scores_gemma":[0.8703443,0.000971971,0.1147811,0.0002438179,0.0003831743,0.0001290896,0.001764955,0.00039972,0.01098189],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008549834,"threshold_uncertainty_score":0.02860206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008031022054361662,"score_gpt":0.2331706152119402,"score_spread":0.2251395931575785,"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."}}