{"id":"W4402289982","doi":"10.2139/ssrn.4949181","title":"Optimizing Rolling Element Bearing Data Collection and Algorithm Hyperparameters for Machine Learning","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Hyperparameter; Element (criminal law); Bearing (navigation); Computer science; Algorithm; Rolling-element bearing; Artificial intelligence; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001471952,0.000340827,0.0003348601,0.0002999163,0.0003162825,0.0004809298,0.0003507166,0.0002022126,0.000005882056],"category_scores_gemma":[0.00004455007,0.0003483528,0.00008884377,0.0001052678,0.00001396594,0.0001527272,0.0005124614,0.004238846,0.000001493154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008580192,"about_ca_system_score_gemma":0.000415859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005980832,"about_ca_topic_score_gemma":0.0001105889,"domain_scores_codex":[0.997564,0.00002917093,0.0004193993,0.0004743887,0.0002055231,0.00130755],"domain_scores_gemma":[0.9994355,0.00005893025,0.0001309721,0.0002439941,0.00005277317,0.00007781231],"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":[0.00001501639,0.000006904612,0.00001734849,0.0003804226,0.0004537076,0.000001551184,0.0002187888,0.8774662,0.00002435207,0.0001602453,0.0000257354,0.1212297],"study_design_scores_gemma":[0.0003683643,0.00008651034,0.000002063777,0.000227865,0.0002323382,0.0001033311,0.0002137075,0.9793605,0.0002206384,0.01753187,0.001308416,0.0003443649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008737035,0.0344002,0.9549277,0.0001150045,0.0009833941,0.0004042978,0.00002982979,0.000302202,0.0001003762],"genre_scores_gemma":[0.6534118,0.1192242,0.2230188,0.00002860176,0.001499832,0.0001357188,0.0008791146,0.0004483232,0.001353585],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7319089,"threshold_uncertainty_score":0.9998968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01561311734448573,"score_gpt":0.2407634069242069,"score_spread":0.2251502895797212,"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."}}