{"id":"W3047930728","doi":"10.1061/9780784483206.012","title":"Prioritizing Pit Cast Iron Small Diameter Watermains for Assessment","year":2020,"lang":"en","type":"article","venue":"Pipelines 2020","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"CIMA+ (Canada)","funders":"","keywords":"Cast iron; Materials science; Metallurgy; Computer science","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":[0.000805134,0.0007095591,0.0004406037,0.00116267,0.0003606014,0.0009324853,0.0007838379,0.0007303227,0.002057177],"category_scores_gemma":[0.001707935,0.0002959639,0.0003631261,0.0003893699,0.000367233,0.001075045,0.0005985104,0.0003117447,0.0003503273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009629848,"about_ca_system_score_gemma":0.001425355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009816986,"about_ca_topic_score_gemma":0.02139607,"domain_scores_codex":[0.9995918,0.00008427051,0.0000118291,0.00005586384,0.0001842068,0.00007203504],"domain_scores_gemma":[0.9992827,0.000303044,0.0001145058,0.00003113029,0.0002048147,0.00006395773],"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":[0.0003477081,0.0001157142,0.02474098,0.00019446,0.00002939059,0.0007556673,0.0002133891,0.8367245,0.05186764,0.005377222,0.001585518,0.07804772],"study_design_scores_gemma":[0.00001245749,0.0003013452,0.00726551,0.000018614,0.00001602325,0.0001179335,0.0001721299,0.9741576,0.01373875,0.002917542,0.00126157,0.00002036259],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.56238,0.0001965354,0.4284019,0.0002378188,0.00003020771,0.0003378907,0.0002829616,0.0005989235,0.007533708],"genre_scores_gemma":[0.9753225,0.00008487399,0.02226257,0.00001086855,0.000003072911,0.00002963237,0.00008153026,0.00002334787,0.002181663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009816986,"threshold_uncertainty_score":0.01951969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05562468979049345,"score_gpt":0.2922905615403029,"score_spread":0.2366658717498094,"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."}}