{"id":"W2342136198","doi":"10.2166/wqrjc.2016.034","title":"Scenario-based quantitative microbial risk assessment to evaluate the robustness of a drinking water treatment plant","year":2016,"lang":"en","type":"article","venue":"Water Quality Research Journal","topic":"Fecal contamination and water quality","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada; University of Waterloo; Wilfrid Laurier University; Natural Sciences and Engineering Research Council of Canada","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Canadian Water Network; Public Health Agency; Polytechnique Montréal; Public Health Agency of Canada","keywords":"Risk analysis (engineering); Robustness (evolution); Risk assessment; Reliability engineering; Computer science; Quality (philosophy); Environmental science; Engineering; Business","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01860254,0.000211765,0.0003150502,0.0001414237,0.0008228777,0.0001925165,0.0006072686,0.00007139256,0.002559661],"category_scores_gemma":[0.0002084391,0.00007440945,0.0002148946,0.0001573276,0.0005484449,0.00034689,0.000362558,0.00041435,0.0004632089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001134018,"about_ca_system_score_gemma":0.00008417837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001400852,"about_ca_topic_score_gemma":0.0006936187,"domain_scores_codex":[0.9904291,0.005868879,0.0007298178,0.000399933,0.001686767,0.0008854808],"domain_scores_gemma":[0.998366,0.0005670744,0.00009951117,0.0004485022,0.0002145025,0.000304433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001272055,0.0008096246,0.04433083,0.00002719613,0.0001981396,0.0000352955,0.009844743,0.005188864,0.923418,0.0003610084,0.001612533,0.01290173],"study_design_scores_gemma":[0.004230639,0.001914218,0.06897248,0.0001737783,0.00005521521,0.00003070917,0.0009230845,0.001961585,0.9047418,0.002059821,0.01448888,0.0004478303],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9694215,0.000004942164,0.01217974,0.01736248,0.0001690977,0.0005381866,0.00004972404,0.00001381141,0.000260534],"genre_scores_gemma":[0.9974502,0.0000159902,0.001351878,0.0001573088,0.00007911714,0.00004396532,0.00001018681,0.00001679765,0.0008745654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0280287,"threshold_uncertainty_score":0.9983521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1641827248327797,"score_gpt":0.429678772062876,"score_spread":0.2654960472300963,"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."}}