{"id":"W7163873974","doi":"10.4050/sm-2024-tvf-5083","title":"Digital Transformation for Cost-Effective Maintenance","year":2024,"lang":"","type":"article","venue":"","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Process (computing); Key (lock); Iterative and incremental development; Digital transformation; Presentation (obstetrics); Transformation (genetics)","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":[0.0009535521,0.0003697165,0.0002139759,0.001040501,0.0003793609,0.001742654,0.0006723378,0.0004127749,0.005120187],"category_scores_gemma":[0.002944232,0.0001149349,0.0002573181,0.0009787654,0.0007100394,0.002161164,0.001488351,0.000444701,0.0008652098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007355784,"about_ca_system_score_gemma":0.0006150327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007561694,"about_ca_topic_score_gemma":0.0008970115,"domain_scores_codex":[0.9990466,0.0002022032,0.00003980942,0.00008533375,0.0005493205,0.00007680597],"domain_scores_gemma":[0.9988033,0.0004791087,0.0001307783,0.0002869428,0.0002595132,0.00004038578],"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":[0.0001727756,0.0002069021,0.002912073,0.00033864,0.00002719893,0.0001949379,0.0005235489,0.04714977,0.0361806,0.1051589,0.003422767,0.803712],"study_design_scores_gemma":[0.0001386111,0.001432907,0.01150973,0.0005225745,0.0001395777,0.001459151,0.003108139,0.4465621,0.1607529,0.123478,0.2507699,0.0001264489],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1967016,0.003283616,0.7212443,0.001988795,0.0002262383,0.0002038914,0.000173454,0.002043217,0.07413488],"genre_scores_gemma":[0.8754847,0.000982526,0.1191004,0.0001179495,0.00004138593,0.00006601751,0.0001129048,0.00009679259,0.003997461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005120187,"threshold_uncertainty_score":0.01712871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01558579051640109,"score_gpt":0.2504033869496521,"score_spread":0.234817596433251,"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."}}