{"id":"W2132475085","doi":"10.1007/s00477-006-0044-7","title":"Investigating evidential reasoning for the interpretation of microbial water quality in a distribution network","year":2006,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Probabilistic logic; Bayesian network; Dempster–Shafer theory; Computer science; Artificial intelligence; Fuzzy logic; Bayesian probability; Sensor fusion; Uncertain data; Data mining; Quality (philosophy); Water quality; Interpretation (philosophy); Machine learning; Mathematics; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01018887,0.0006912503,0.0008280504,0.0008930209,0.0009519115,0.003595606,0.001789215,0.001920183,0.001940999],"category_scores_gemma":[0.05116878,0.0005375594,0.001389167,0.0008134073,0.003112822,0.005320016,0.001891416,0.002677996,0.00009078474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002502542,"about_ca_system_score_gemma":0.001520611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005029025,"about_ca_topic_score_gemma":0.004057566,"domain_scores_codex":[0.9965193,0.002407787,0.0001837184,0.0003010131,0.0004285077,0.0001596082],"domain_scores_gemma":[0.9538987,0.04203392,0.001909748,0.0008377188,0.0009880149,0.0003319299],"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":[0.0001286016,0.00006342825,0.001855656,0.0001217638,0.0001015033,0.0002560363,0.0006933,0.6281857,0.000720205,0.3552522,0.0003352605,0.01228635],"study_design_scores_gemma":[0.00001700889,0.00001490182,0.0001622179,0.00001403044,0.00001480144,0.00001734657,0.00007114205,0.7541279,0.0001726649,0.2451782,0.0002008811,0.000008894202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1612414,0.0003632429,0.8316265,0.00215222,0.00003909824,0.00003909134,0.00007235527,0.00004963983,0.004416496],"genre_scores_gemma":[0.9205111,0.0001554695,0.07855985,0.00008048327,0.00004163174,0.0000371948,0.00005429145,0.0000173828,0.000542582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01018887,"threshold_uncertainty_score":0.05388451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1058617582412894,"score_gpt":0.4721240382860874,"score_spread":0.366262280044798,"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."}}