{"id":"W4387258465","doi":"10.26226/m.634060663701db4fe35304b0","title":"Improving Electricity Conservation in Small-Medium Municipal Water Distribution Systems in Ontario, Canada","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Electricity; Distribution (mathematics); Water conservation; Electric power distribution; Environmental science; Electricity system; Mains electricity; Business; Environmental protection; Environmental planning; Water resource management; Geography; Electricity generation; Water resources; Engineering; Ecology; Mathematics; Electrical engineering; Power (physics); Physics","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":[0.0004236737,0.0002565582,0.0003295266,0.0004712674,0.001623367,0.001502407,0.0006641282,0.000338132,0.003404691],"category_scores_gemma":[0.001801706,0.0001925946,0.0002620154,0.001515944,0.0005908042,0.0004237755,0.0003137872,0.0003440434,0.0001370336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03628755,"about_ca_system_score_gemma":0.02714616,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9938626,"about_ca_topic_score_gemma":0.9974152,"domain_scores_codex":[0.9995797,0.00005427042,0.00001275777,0.00005040285,0.0001251245,0.0001777674],"domain_scores_gemma":[0.9990976,0.0001829187,0.00004942024,0.00002407127,0.0004966077,0.0001494126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0009955232,0.0005070379,0.1905406,0.000415691,0.0001511474,0.0007944503,0.001041265,0.6411006,0.006883498,0.01420018,0.03326391,0.1101061],"study_design_scores_gemma":[0.0002178459,0.0001783118,0.2797308,0.00007497671,0.0001117172,0.00007250524,0.004290354,0.6833385,0.00504316,0.003257289,0.02361949,0.00006504499],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819803,0.000304724,0.002264779,0.000886589,0.00001795715,0.0000964439,0.00118536,0.00007896612,0.01318492],"genre_scores_gemma":[0.9885681,0.0001553224,0.001407504,0.00002790581,0.000003198128,0.00001028265,0.0004087941,0.00001628421,0.009402763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03628755,"threshold_uncertainty_score":0.2632858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01167803184653132,"score_gpt":0.1704399519353805,"score_spread":0.1587619200888492,"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."}}