{"id":"W2970613315","doi":"10.14778/3352063.3352096","title":"ApproxML","year":2019,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Pipeline (software); Reuse; Machine learning; Artificial intelligence; Variety (cybernetics); Mixture model; Process (computing); Gaussian process; Gaussian","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":[0.005810534,0.001538713,0.001403636,0.002194084,0.001168698,0.007573531,0.004818378,0.002800044,0.05055661],"category_scores_gemma":[0.03722121,0.001145105,0.002387057,0.002321703,0.001537135,0.009824325,0.006799347,0.004030102,0.0219881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002049335,"about_ca_system_score_gemma":0.003166516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002883287,"about_ca_topic_score_gemma":0.004464619,"domain_scores_codex":[0.9925761,0.002423257,0.0006345114,0.001416069,0.002615802,0.0003343142],"domain_scores_gemma":[0.9823166,0.007600474,0.0006665563,0.006768756,0.002322465,0.0003252115],"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":[0.0007548995,0.0002271176,0.002048201,0.000903399,0.0002131793,0.0004001027,0.000406142,0.05949151,0.002805402,0.2808711,0.1548818,0.496997],"study_design_scores_gemma":[0.0001132359,0.00007190247,0.0002335964,0.0001284594,0.00005632334,0.0004179195,0.0000945618,0.4581991,0.007067977,0.3764597,0.1570941,0.00006300129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001414521,0.0004881291,0.9463077,0.001008549,0.0002046146,0.0001344716,0.004344051,0.03734247,0.008755592],"genre_scores_gemma":[0.1002454,0.001231351,0.8502906,0.002089706,0.0004710041,0.0005352881,0.02056257,0.009915202,0.01465884],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05055661,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007709354976284009,"score_gpt":0.1924491539718222,"score_spread":0.1847397989955382,"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."}}