{"id":"W2743707033","doi":"10.1061/9780784480885.013","title":"Collection and Compilation of Water Pipeline Field Performance Data","year":2017,"lang":"en","type":"article","venue":"Pipelines 2017","topic":"Water Systems and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Pipeline (software); Field (mathematics); Database; Programming language","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":[0.004547361,0.000872869,0.0009017281,0.009116467,0.0008638523,0.001833861,0.001686525,0.000539218,0.006250876],"category_scores_gemma":[0.01350747,0.0006220144,0.0005976571,0.009468014,0.0005221932,0.002269242,0.001380629,0.00103298,0.004472282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001979565,"about_ca_system_score_gemma":0.006493119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02523642,"about_ca_topic_score_gemma":0.02199265,"domain_scores_codex":[0.9944016,0.0006459201,0.0008322608,0.0007957997,0.003057618,0.0002667559],"domain_scores_gemma":[0.9739498,0.002543854,0.002202639,0.004314022,0.01631494,0.0006747706],"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":[0.0004085392,0.001753268,0.1720472,0.001937991,0.0001635224,0.0006774209,0.002760772,0.04232022,0.02258698,0.005965197,0.1862877,0.5630913],"study_design_scores_gemma":[0.0001963956,0.0009892361,0.4742541,0.0007666919,0.0001504598,0.0003912474,0.004797695,0.04225756,0.0397849,0.00559045,0.4304598,0.0003615377],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1397804,0.0002595726,0.173194,0.0006335168,0.0001897067,0.00866292,0.6279365,0.009864215,0.03947916],"genre_scores_gemma":[0.1883042,0.0005804296,0.2044208,0.0002198714,0.0001064773,0.009278579,0.5864724,0.001497934,0.0091192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02523642,"threshold_uncertainty_score":0.05017906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04132809063774565,"score_gpt":0.2524838837480644,"score_spread":0.2111557931103188,"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."}}