{"id":"W2138921861","doi":"10.5194/dwes-3-71-2010","title":"I-WARP: Individual Water mAin Renewal Planner","year":2010,"lang":"en","type":"article","venue":"Drinking water engineering and science","topic":"Water Systems and Optimization","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"American Water Works Association Research Foundation; Water Research Foundation","keywords":"Breakage; Mains electricity; Foundation (evidence); Engineering; Electricity; Civil engineering; Geotechnical engineering; Environmental science; Forensic engineering; Geography; Computer science; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001032163,0.0006312046,0.0007018466,0.0005421961,0.0004219297,0.001012848,0.001622384,0.0009673975,0.02336106],"category_scores_gemma":[0.001972563,0.0004882975,0.0005399632,0.000673331,0.0005691493,0.001401964,0.001767804,0.001221238,0.001239784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001438639,"about_ca_system_score_gemma":0.002075514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006101187,"about_ca_topic_score_gemma":0.00586481,"domain_scores_codex":[0.9996746,0.00009090515,0.0000110301,0.00006450408,0.00005991628,0.0000990086],"domain_scores_gemma":[0.9994065,0.0002513936,0.00007056651,0.00005122438,0.0000780826,0.0001424127],"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":[0.0001289291,0.00008040547,0.0006231843,0.00006106103,0.00002028514,0.0000852423,0.00005564481,0.9382254,0.0003905378,0.02371595,0.00531058,0.03130269],"study_design_scores_gemma":[0.00002795179,0.00006052564,0.00009815487,0.000008590913,0.000007069348,0.00002397636,0.00004890025,0.9868586,0.0002500852,0.009119404,0.00349081,0.000006054778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09730228,0.0003419756,0.8276142,0.001691624,0.00009579227,0.001052131,0.002014075,0.002491356,0.06739657],"genre_scores_gemma":[0.853525,0.0002266754,0.1204353,0.0001843517,0.00003170998,0.0006753125,0.0006024723,0.0002244541,0.02409473],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02336106,"threshold_uncertainty_score":0.07815051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00524188546840148,"score_gpt":0.1687715120456299,"score_spread":0.1635296265772284,"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."}}