{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0218561,0.001852496,0.001404054,0.005433577,0.001106462,0.007031887,0.006470766,0.003881918,0.001943827],"category_scores_gemma":[0.05914772,0.00155476,0.001262172,0.005934992,0.003724919,0.009329772,0.003295792,0.007269939,0.004746008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002763125,"about_ca_system_score_gemma":0.002468904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005416417,"about_ca_topic_score_gemma":0.005295082,"domain_scores_codex":[0.9822137,0.00579611,0.001807707,0.003627793,0.006105657,0.0004490184],"domain_scores_gemma":[0.9603934,0.01966829,0.001241846,0.01007235,0.007844136,0.0007799483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001316237,0.0002703728,0.005236295,0.002491949,0.0002076031,0.0001569327,0.0005133245,0.04145842,0.002585975,0.05201954,0.04545941,0.8494686],"study_design_scores_gemma":[0.00007574935,0.0002987178,0.004504118,0.00446027,0.0001078559,0.001008265,0.0007797875,0.2303195,0.02107531,0.2996635,0.4374335,0.0002733537],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009873287,0.1017049,0.8375919,0.02054651,0.000900408,0.0002483897,0.000946375,0.006669409,0.02151881],"genre_scores_gemma":[0.09313854,0.06223656,0.8334779,0.002621891,0.001086189,0.0002525334,0.002779103,0.001389526,0.00301777],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0218561,"threshold_uncertainty_score":0.1155875,"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."}}