{"id":"W4391887961","doi":"10.1016/j.jmsy.2024.02.006","title":"Pipeline condition monitoring towards digital twin system: A case study","year":2024,"lang":"en","type":"article","venue":"Journal of Manufacturing Systems","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Research Council Canada","keywords":"Pipeline (software); Computer science; Cloud computing; Engineering; Pipeline transport; Real-time computing; Mechanical engineering","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":[0.0003958013,0.0004999542,0.0003527958,0.0008252733,0.0007738638,0.0007900922,0.0008184341,0.001954172,0.002047224],"category_scores_gemma":[0.001991195,0.0002368838,0.0003250343,0.0007987246,0.0005410613,0.0008275231,0.0005422147,0.0005958703,0.0002992176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005725367,"about_ca_system_score_gemma":0.0004743959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003721781,"about_ca_topic_score_gemma":0.004943431,"domain_scores_codex":[0.9995385,0.0000746829,0.00003567616,0.0001148555,0.0001718021,0.0000644897],"domain_scores_gemma":[0.9988509,0.000524384,0.0001772665,0.0001446883,0.0001808502,0.0001218528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003538806,0.00151871,0.186351,0.001267395,0.0002667333,0.2454679,0.00531048,0.1578691,0.100823,0.008614386,0.01134882,0.2776236],"study_design_scores_gemma":[0.0002345981,0.002952553,0.06883509,0.0001489258,0.000346901,0.1178472,0.005297452,0.5813926,0.1920586,0.007764815,0.02289524,0.0002259363],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9392648,0.0003333605,0.05487132,0.0006271201,0.00005160527,0.0001006753,0.000388278,0.0006430473,0.00371988],"genre_scores_gemma":[0.9921792,0.00009799298,0.005873372,0.00003048667,0.00001059624,0.000007456033,0.00007233885,0.00002274696,0.001705791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003721781,"threshold_uncertainty_score":0.007400215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01415422539893783,"score_gpt":0.2925176079550322,"score_spread":0.2783633825560943,"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."}}