{"id":"W4225413438","doi":"10.2166/wst.2022.115","title":"Hybrid modelling of water resource recovery facilities: status and opportunities","year":2022,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Japan Society for the Promotion of Science; Vlaamse regering; Fonds Wetenschappelijk Onderzoek","keywords":"Computer science; Leverage (statistics); Resource (disambiguation); Interpretability; Risk analysis (engineering); Process (computing); Systems engineering; Management science; Engineering; Business; Artificial intelligence","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":[0.0006141036,0.0000990859,0.0001562496,0.0007954419,0.0001861482,0.00002478785,0.0002904585,0.00003316454,0.00006324149],"category_scores_gemma":[0.0000113044,0.00007555044,0.00002060731,0.0001771611,0.000323964,0.0001687244,0.000284111,0.0001909213,0.000001730068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006466995,"about_ca_system_score_gemma":0.00001587606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000129536,"about_ca_topic_score_gemma":1.528354e-7,"domain_scores_codex":[0.9989393,0.00001989567,0.0002059045,0.0001903505,0.0002095153,0.0004349796],"domain_scores_gemma":[0.9996044,0.0000186311,0.00001111503,0.0002834116,0.00003169173,0.00005077625],"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":[0.000003362039,0.000002942832,0.0000362902,0.00002653529,0.000003617841,0.000003791642,0.001047387,0.9778382,0.01898832,0.0002856596,0.00001251887,0.001751338],"study_design_scores_gemma":[0.0001210904,0.00006941947,0.000001728704,0.000004676205,0.000002761253,0.00001914067,0.001173378,0.6675339,0.2683003,0.003966102,0.05866733,0.0001400975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9103685,0.0002033211,0.08807952,0.00013354,0.0001327928,0.0000580532,0.00001471864,0.0003117419,0.0006978138],"genre_scores_gemma":[0.9954255,0.0000678317,0.003822176,0.000009961711,0.000005983845,0.00002191045,0.000005627305,0.0000128348,0.0006281565],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3103043,"threshold_uncertainty_score":0.3080858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0385104550915231,"score_gpt":0.2312273389480366,"score_spread":0.1927168838565135,"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."}}