{"id":"W4378965591","doi":"10.18280/jesa.560219","title":"Performance Monitoring of CNC Machine Using Modelsim","year":2023,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"ModelSim; Computer science; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000343284,0.0006520271,0.0006169428,0.0004439972,0.0002774373,0.0005743091,0.0008080062,0.0006829759,0.003019151],"category_scores_gemma":[0.001046211,0.0002126605,0.0004830933,0.000330035,0.0002547141,0.0004915511,0.0002752303,0.0005319461,0.000457159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005343769,"about_ca_system_score_gemma":0.0006866562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01022322,"about_ca_topic_score_gemma":0.006368716,"domain_scores_codex":[0.999769,0.00004773305,0.00001388261,0.00004357124,0.0001023301,0.0000234969],"domain_scores_gemma":[0.9996166,0.000138558,0.00005023157,0.00005195193,0.0001299026,0.00001263726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0000742964,0.00004300221,0.001031827,0.0001219437,0.00002772542,0.00003828469,0.00005242387,0.9740288,0.006113861,0.001534669,0.0008467759,0.01608645],"study_design_scores_gemma":[0.000003312938,0.00001786025,0.0001788518,0.000003197055,0.000002891211,0.000006484451,0.00000318759,0.9975129,0.001578065,0.0001423835,0.0005474049,0.000003493678],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2230234,0.0004423485,0.7386063,0.0004270717,0.0001595809,0.0002238309,0.001066627,0.008764684,0.02728615],"genre_scores_gemma":[0.9596736,0.0001559862,0.03590186,0.00003067347,0.00001023478,0.0002118079,0.0004641766,0.0001357374,0.00341598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01022322,"threshold_uncertainty_score":0.02032739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03123295767625808,"score_gpt":0.2569914968035092,"score_spread":0.2257585391272512,"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."}}