{"id":"W4378549716","doi":"10.1016/j.cirp.2023.05.006","title":"Digital twins for cutting processes","year":2023,"lang":"en","type":"article","venue":"CIRP Annals","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Process (computing); Computer science; Data science; Production (economics); Industrial production; Systems engineering; Industrial engineering; Manufacturing engineering; Process management; Engineering","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":[0.0004389972,0.0005773263,0.000424977,0.001362653,0.001433185,0.002842547,0.0008000428,0.001270015,0.0354788],"category_scores_gemma":[0.001636605,0.0003185591,0.0004175579,0.001307143,0.0008270603,0.003325205,0.003255533,0.001519791,0.006414789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006518526,"about_ca_system_score_gemma":0.0007805281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003971578,"about_ca_topic_score_gemma":0.0007351636,"domain_scores_codex":[0.9993966,0.00007716133,0.00002795534,0.0001103542,0.0003125114,0.00007542703],"domain_scores_gemma":[0.9996197,0.00006579522,0.00002650004,0.0001506024,0.00008454955,0.0000527889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000246709,0.00008164698,0.0002590528,0.0001704664,0.000009911158,0.0002024725,0.0001833934,0.002001706,0.0149368,0.683705,0.01831319,0.2798896],"study_design_scores_gemma":[0.00005543188,0.0002889585,0.0007317018,0.0001850346,0.00004263295,0.0009119631,0.0003750431,0.02280108,0.04571947,0.3169769,0.6118507,0.00006109207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.07262076,0.006991934,0.3720125,0.002951602,0.007293061,0.000237306,0.0006575966,0.002515259,0.53472],"genre_scores_gemma":[0.4493544,0.005329819,0.2214778,0.001061696,0.001000741,0.000227926,0.0008870227,0.001080152,0.3195804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0354788,"threshold_uncertainty_score":0.1186884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03781769798517049,"score_gpt":0.2676878070913056,"score_spread":0.2298701091061351,"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."}}