{"id":"W4236819933","doi":"10.1109/wsc.2015.7408329","title":"Machine learning-based metamodels for sawing simulation","year":2015,"lang":"en","type":"article","venue":"2015 Winter Simulation Conference (WSC)","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Exploit; Machine learning; Kernel (algebra); Decision tree; Artificial intelligence; Ridge; Regression; k-nearest neighbors algorithm; Tree (set theory); Kernel method; Data mining; Support vector machine; Mathematics","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":[0.001557282,0.0007090279,0.0008301222,0.0008302503,0.0004530138,0.0009085321,0.00159996,0.001397098,0.003224623],"category_scores_gemma":[0.006170252,0.0004409874,0.001195672,0.0005535682,0.0007304031,0.001149198,0.0009012136,0.001389261,0.0004922354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001367588,"about_ca_system_score_gemma":0.001026201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003684055,"about_ca_topic_score_gemma":0.002988666,"domain_scores_codex":[0.999373,0.0002394793,0.00004636214,0.00009951957,0.0001900554,0.00005158922],"domain_scores_gemma":[0.9968003,0.001803787,0.0002888334,0.0005326755,0.0004425755,0.0001317129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001216729,0.00001729132,0.0002849503,0.00001034582,0.000005448524,0.000009871065,0.000007250133,0.9942442,0.0003833607,0.002483487,0.00008963249,0.002451987],"study_design_scores_gemma":[0.000001637637,0.000004944994,0.00002171801,0.000001318257,5.18069e-7,0.000002052981,0.000001008856,0.9982999,0.0002629655,0.001285453,0.0001169766,0.000001480856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04439144,0.00009788345,0.9504108,0.0002236727,0.00003779636,0.0001073903,0.0004858715,0.001951271,0.002293883],"genre_scores_gemma":[0.6893443,0.0001397124,0.3067095,0.0001393316,0.00002699645,0.0004339798,0.001310564,0.0004247279,0.001470782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003684055,"threshold_uncertainty_score":0.01078743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09454771569774759,"score_gpt":0.334741822767288,"score_spread":0.2401941070695404,"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."}}