{"id":"W2324701914","doi":"10.1061/40941(247)148","title":"Use of Bayesian Statistics to Study Chlorine Decay within a Water Distribution System","year":2008,"lang":"en","type":"article","venue":"","topic":"Water Systems and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Canada Research Chairs","keywords":"Chlorine; Monte Carlo method; Markov chain Monte Carlo; Bayesian probability; Markov process; Component (thermodynamics); Residual; Statistical physics; Chemistry; Biological system; Computer science; Mathematics; Statistics; Algorithm; Thermodynamics; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0000813552,0.00009160495,0.0001503926,0.00003875747,0.00003746526,0.00001792133,0.0000505339,0.00002706802,0.00001470902],"category_scores_gemma":[0.000004187705,0.00006389694,0.00001313592,0.00008685048,0.000005349047,0.00009403526,0.00001958468,0.00002998966,0.0000210138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005344275,"about_ca_system_score_gemma":0.00000338258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002305727,"about_ca_topic_score_gemma":0.0001769064,"domain_scores_codex":[0.9993386,0.00001722685,0.0002911307,0.00009322893,0.0001368835,0.0001229411],"domain_scores_gemma":[0.9996884,0.000008559366,0.00001615334,0.0001589148,0.00006997264,0.00005799362],"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":[0.00002993212,0.0001266597,0.01506246,0.0002558831,0.0001190998,0.00006510102,0.005996384,0.9695388,0.001022925,0.001804785,0.005851921,0.0001260311],"study_design_scores_gemma":[0.001268955,0.0005586409,0.02579891,0.0001185102,0.00008154899,0.0000908999,0.00123958,0.7414486,0.2267825,0.00000580607,0.00192515,0.0006808383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4127497,0.000001925305,0.5865633,0.000003082231,0.000184909,0.0002429033,0.00004964061,0.000115871,0.00008862837],"genre_scores_gemma":[0.9935741,9.394981e-7,0.005872508,0.000002071496,0.00003138659,0.0000198418,0.00008218492,0.00001714865,0.0003997863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5808245,"threshold_uncertainty_score":0.2605641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01964095412161654,"score_gpt":0.1964116669034087,"score_spread":0.1767707127817921,"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."}}