{"id":"W2465665672","doi":"10.1061/9780784479957.052","title":"PIPEiD: Pipeline Infrastructure Database","year":2016,"lang":"en","type":"article","venue":"Pipelines 2016","topic":"Water Systems and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"American Water (Canada)","funders":"","keywords":"Pipeline (software); Asset (computer security); Sustainability; Computer science; Asset management; Database; Engineering; Engineering management; Computer security; Business; Finance","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":[0.005975864,0.00168384,0.001427262,0.005182502,0.001369588,0.007170582,0.006870978,0.002248461,0.07871939],"category_scores_gemma":[0.02045917,0.001543074,0.0009144315,0.006352166,0.000746061,0.01238665,0.007032114,0.003247988,0.07785028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002189413,"about_ca_system_score_gemma":0.005049811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00707797,"about_ca_topic_score_gemma":0.003584754,"domain_scores_codex":[0.9948166,0.0006980572,0.0008558561,0.0009322158,0.002293771,0.0004034055],"domain_scores_gemma":[0.9911336,0.001859193,0.0008120716,0.002647411,0.002586152,0.0009615611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002517328,0.0000527403,0.001215409,0.0005224287,0.00003210487,0.0001423855,0.0002209044,0.001122147,0.001245464,0.0149117,0.9316559,0.04862693],"study_design_scores_gemma":[0.0001403533,0.00004672939,0.001271095,0.0001264117,0.0000209379,0.0001887222,0.0001216557,0.007000301,0.003459703,0.007664539,0.9798758,0.00008380313],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"dataset","genre_scores_codex":[0.003017154,0.001223352,0.1789225,0.00404624,0.0006307333,0.001402713,0.3342059,0.3798877,0.09666376],"genre_scores_gemma":[0.05132056,0.001760047,0.09468566,0.003557442,0.0003978827,0.001858027,0.785753,0.0264764,0.03419101],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.07871939,"threshold_uncertainty_score":0.2633426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006340815419037985,"score_gpt":0.1915822392407346,"score_spread":0.1852414238216966,"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."}}