{"id":"W2001168879","doi":"10.1002/hyp.5763","title":"Short‐term municipal water demand forecasting","year":2005,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Water resources management and optimization","field":"Engineering","cited_by":254,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Science Council","keywords":"Demand forecasting; Sizing; Regression analysis; Environmental science; Water supply; Time series; Term (time); Water resources; Supply and demand; Demand management; Regression; Population; Linear regression; Artificial neural network; Hydrology (agriculture); Econometrics; Water resource management; Computer science; Operations research; Environmental engineering; Statistics; Engineering; Mathematics; Economics; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008029467,0.0001312105,0.0001173042,0.00004718822,0.00007435821,0.00006078387,0.0001537076,0.00007081682,0.0001833225],"category_scores_gemma":[0.0000167731,0.0000844961,0.00002554784,0.00008500058,0.00003161993,0.0002001142,0.00006385245,0.00009484802,0.00007974521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001366749,"about_ca_system_score_gemma":8.539608e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.485911e-7,"about_ca_topic_score_gemma":0.00000835008,"domain_scores_codex":[0.9992822,0.000009048882,0.0001615447,0.0001480867,0.00009818568,0.0003009454],"domain_scores_gemma":[0.9998131,0.00001809945,0.00000825918,0.0000930967,0.00001812006,0.00004930929],"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":[0.00001225918,0.00002843262,0.002203558,0.0001692823,0.00002150737,0.00001148267,0.0006091681,0.9901283,0.000390539,0.00001322082,0.0001946425,0.006217656],"study_design_scores_gemma":[0.000258224,0.0000685964,0.0004891485,0.00002397146,0.00003440445,0.00001165854,0.00001994267,0.9572671,0.01352618,0.0003359534,0.02762806,0.0003367412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9760045,0.0003115864,0.005539189,0.0001108926,0.00003785652,0.0001149494,7.18358e-7,0.0004809213,0.01739933],"genre_scores_gemma":[0.9986872,0.00006440301,0.000664305,0.00009393581,0.0001910966,0.00002263216,0.00002610883,0.00001816706,0.000232131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03286114,"threshold_uncertainty_score":0.3445651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02981126545751057,"score_gpt":0.2127175381763568,"score_spread":0.1829062727188462,"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."}}