{"id":"W2587576867","doi":"10.5942/jawwa.2017.109.0059","title":"Using Existing Municipal Water Data to Support Conservation Efforts","year":2017,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water resources management and optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Limiting; Process (computing); Sustainability; Data collection; Water conservation; Business; Water resources; Environmental planning; Environmental resource management; Computer science; Engineering; Environmental science","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":[0.008964694,0.0004527649,0.0004753855,0.007255665,0.002288015,0.007337729,0.001457463,0.0005307295,0.003860254],"category_scores_gemma":[0.03956908,0.0004016768,0.000275053,0.01359353,0.001021659,0.004770588,0.003026616,0.000999485,0.0007831936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00572721,"about_ca_system_score_gemma":0.01445068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1110919,"about_ca_topic_score_gemma":0.2259663,"domain_scores_codex":[0.992124,0.003845934,0.0005083242,0.0006278185,0.002377055,0.000516851],"domain_scores_gemma":[0.9699084,0.01004626,0.003382916,0.006532004,0.009027211,0.001103283],"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":[0.0001624919,0.0006671836,0.1870683,0.0008088728,0.0001750487,0.0004632031,0.01091946,0.01972891,0.003996036,0.03417442,0.02868908,0.7131471],"study_design_scores_gemma":[0.0001146454,0.0004241133,0.1748767,0.002845162,0.0003368063,0.0002203383,0.06481415,0.08440725,0.01675647,0.05734489,0.5975641,0.0002954039],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5113779,0.001730689,0.1199619,0.01980955,0.0003819947,0.002554776,0.01291507,0.002970155,0.3282982],"genre_scores_gemma":[0.894655,0.001018935,0.09261219,0.0003097233,0.00004832984,0.0003274353,0.005030972,0.000194811,0.005802668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1110919,"threshold_uncertainty_score":0.2208906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0790533799174284,"score_gpt":0.2987233608454927,"score_spread":0.2196699809280643,"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."}}