{"id":"W2783243595","doi":"10.1103/physreve.96.062317","title":"Organization and scaling in water supply networks","year":2017,"lang":"en","type":"article","venue":"Physical review. E","topic":"Sustainability and Ecological Systems Analysis","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Scaling; Water supply; Resource (disambiguation); Allometry; Probabilistic logic; Range (aeronautics); Computer science; Environmental science; Ecology; Mathematics; Environmental engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002696289,0.00006010216,0.0001824852,0.000004137863,0.0001667256,0.00004276772,0.0001345254,0.00002069475,0.0005583515],"category_scores_gemma":[0.0002892861,0.0000366997,0.00003078952,0.0000657995,0.00008653984,0.0001560452,0.0002117883,0.0000641864,0.0001420581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000517025,"about_ca_system_score_gemma":9.380479e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002591655,"about_ca_topic_score_gemma":0.00006054391,"domain_scores_codex":[0.999419,0.00004524849,0.0001164876,0.0001817168,0.00008142066,0.0001561564],"domain_scores_gemma":[0.9996716,0.00002774523,0.00003768572,0.0002075949,0.000005893857,0.00004949083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000001640969,0.00008140354,0.9750098,0.0001011943,0.000004188891,0.000005239981,0.0001173907,0.0006921777,0.000432848,0.0002631201,0.0001817578,0.02310923],"study_design_scores_gemma":[0.0001134365,0.00001896341,0.979789,0.0001075786,0.00002992003,0.000001310435,0.0000191089,0.01158547,0.0002512205,0.004145487,0.00379167,0.0001468631],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976165,0.0002078672,0.0001357354,0.001075514,0.00001518231,0.0001418287,1.205001e-7,0.000007256952,0.0007999412],"genre_scores_gemma":[0.9988461,0.0008111548,0.00001557894,0.0002205488,0.00004318858,0.000008684259,0.000002672368,0.000003462233,0.00004866039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02296237,"threshold_uncertainty_score":0.6113558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006935046235357899,"score_gpt":0.2682054519405688,"score_spread":0.2612704057052109,"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."}}