{"id":"W4313404444","doi":"10.3390/machines10121233","title":"Machine Learning in CNC Machining: Best Practices","year":2022,"lang":"en","type":"article","venue":"Machines","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Machining; Leverage (statistics); Software deployment; Computer science; Software; Numerical control; Machine tool; Focus (optics); Machine learning; Artificial intelligence; Industrial engineering; Software engineering; Engineering; Mechanical engineering; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0006197664,0.0001463509,0.0001905949,0.0002509148,0.0002562147,0.00005462725,0.0001296589,0.00005191523,0.0005213076],"category_scores_gemma":[0.0001597599,0.0001446157,0.00005366797,0.0004205527,0.000007648505,0.0001416238,0.00008462926,0.0008084614,0.00005683293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009210316,"about_ca_system_score_gemma":0.0000126312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002047222,"about_ca_topic_score_gemma":0.0002334732,"domain_scores_codex":[0.9989058,0.000188923,0.0002740856,0.000179183,0.0002388618,0.000213151],"domain_scores_gemma":[0.9995762,0.0001117778,0.0001210081,0.000137855,0.00001111902,0.00004207068],"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":[0.0001231671,0.0001199446,0.1529954,0.00009006187,0.00005729464,0.0001263125,0.001289618,0.6754419,0.006900403,0.0003027021,0.001163706,0.1613895],"study_design_scores_gemma":[0.0009748609,0.0002473968,0.002459601,0.00002190874,0.00001487144,0.0001338673,0.0005529173,0.5293273,0.000222928,0.00008723575,0.4655853,0.0003718834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9550091,0.001906666,0.0002597384,0.0001493635,0.002125956,0.0002623942,0.00001946611,0.0006147253,0.0396526],"genre_scores_gemma":[0.9976396,0.00002320586,0.0000561146,0.00002660951,0.0002110907,0.00006413199,0.00001828707,0.0000402058,0.001920778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4644215,"threshold_uncertainty_score":0.5897259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04154048220963034,"score_gpt":0.2808579207850636,"score_spread":0.2393174385754332,"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."}}