{"id":"W3125483663","doi":"10.1115/ipc2020-9373","title":"Distribution Reliability Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Geotechnical Engineering and Underground Structures","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Pipeline (software); Computer science; Reliability engineering; Excavation; Distribution (mathematics); Engineering; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001861022,0.0009654771,0.0008832524,0.002842576,0.0004169766,0.00129061,0.0009185725,0.0008516589,0.003294678],"category_scores_gemma":[0.008405455,0.0003532688,0.0008744024,0.001604965,0.0004244075,0.0007818346,0.0007199678,0.001191424,0.001136648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001369548,"about_ca_system_score_gemma":0.00106237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01766827,"about_ca_topic_score_gemma":0.01171982,"domain_scores_codex":[0.9990311,0.0003776172,0.00006302984,0.000234545,0.0001832725,0.0001104996],"domain_scores_gemma":[0.9947774,0.003304761,0.0003801046,0.0003910651,0.001046585,0.0001000791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008064034,0.0001084593,0.01059371,0.00005803611,0.0001073329,0.00007529378,0.00003048143,0.8460182,0.0003248243,0.001545829,0.003419078,0.1376382],"study_design_scores_gemma":[0.000001784922,0.000008199657,0.000473165,0.000005468414,0.000004472632,0.000005391228,0.000004614784,0.9976567,0.0001053798,0.001540522,0.0001918104,0.000002489081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1480441,0.00134398,0.8338633,0.001318323,0.000182356,0.0002019305,0.001855072,0.005012869,0.008178081],"genre_scores_gemma":[0.9425378,0.0002579684,0.05198399,0.00009434945,0.0001011755,0.000125819,0.001588379,0.00009152191,0.003219101],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01766827,"threshold_uncertainty_score":0.03513086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01022545251381986,"score_gpt":0.1963836438300181,"score_spread":0.1861581913161983,"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."}}