{"id":"W4385945537","doi":"10.2139/ssrn.4544162","title":"Sampling Balanced High Quality Data to Train an Automatic Mesh Generator for its Optimal Performance","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Generator (circuit theory); Computer science; Sampling (signal processing); Quality (philosophy); Data quality; Reliability engineering; Engineering; Operations management; Power (physics); Telecommunications; Physics","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":[0.0008902488,0.0007966593,0.0006769361,0.001046725,0.0003549911,0.0004969664,0.001132221,0.001228392,0.0028708],"category_scores_gemma":[0.00536928,0.0004461303,0.0005705489,0.001118871,0.0004252803,0.0007919204,0.0009051402,0.001194636,0.001171145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004122535,"about_ca_system_score_gemma":0.0008397091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003565449,"about_ca_topic_score_gemma":0.003758703,"domain_scores_codex":[0.9995448,0.00009666443,0.00002342639,0.0001475733,0.0001323178,0.0000551711],"domain_scores_gemma":[0.9985687,0.0006981107,0.0000703922,0.0002730649,0.0003350607,0.00005465195],"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":[0.0005877659,0.0001446387,0.0033324,0.00014134,0.00008627887,0.0001106377,0.00008597446,0.3422792,0.03556957,0.005557865,0.007643549,0.6044608],"study_design_scores_gemma":[0.00001129068,0.00002427086,0.0002691877,0.000002632375,0.00000506035,0.00002069412,0.00000569909,0.9935132,0.004087778,0.001674043,0.0003829298,0.000003336373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0263768,0.0001284248,0.9706908,0.0001250869,0.00009070016,0.00005082717,0.0001966902,0.001844204,0.0004965224],"genre_scores_gemma":[0.4783512,0.0001153819,0.5168654,0.0001392694,0.0001029718,0.0002058688,0.001625269,0.0004036485,0.002190982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003565449,"threshold_uncertainty_score":0.009603798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1208635663890689,"score_gpt":0.3748414382617501,"score_spread":0.2539778718726813,"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."}}