{"id":"W4224879627","doi":"10.1016/j.watres.2022.118480","title":"Comparing quantitative probability of occurrence to a risk matrix approach: A study of chlorine residual data","year":2022,"lang":"en","type":"article","venue":"Water Research","topic":"Water Systems and Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Risk assessment; Risk management; Matrix (chemical analysis); Residual; Statistics; Risk analysis (engineering); Computer science; Data mining; Data Matrix; Reliability engineering; Engineering; Mathematics; Algorithm; Chemistry","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":[0.00855055,0.0003236472,0.0005271892,0.002145262,0.0003031439,0.001246386,0.001084683,0.0007883121,0.001329451],"category_scores_gemma":[0.03056217,0.0001849815,0.0007556169,0.00188895,0.0006332754,0.001692192,0.0004742463,0.0007352606,0.00008744802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009620956,"about_ca_system_score_gemma":0.0006033737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007357982,"about_ca_topic_score_gemma":0.003906289,"domain_scores_codex":[0.9970499,0.001821238,0.000112087,0.0002878438,0.000607548,0.000121311],"domain_scores_gemma":[0.9113755,0.08235756,0.001699626,0.002098658,0.002210842,0.0002578554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001453239,0.0004302947,0.03846011,0.0003224677,0.0003573588,0.0002082031,0.0002807168,0.9001577,0.004618726,0.01434342,0.000830408,0.0385373],"study_design_scores_gemma":[0.00001817678,0.0002995262,0.008741493,0.000007992205,0.00003944036,0.00007363666,0.0001279128,0.9852962,0.001427517,0.003745785,0.0001992504,0.00002301167],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9120519,0.0002583714,0.08564931,0.0001426492,0.0000119684,0.00004501058,0.0003422783,0.0001610316,0.001337572],"genre_scores_gemma":[0.9882231,0.00004757748,0.0111125,0.000009561242,0.00000661413,0.00001277972,0.0002842213,0.00003101946,0.0002726139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00855055,"threshold_uncertainty_score":0.0452202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3030283941571174,"score_gpt":0.390153396217937,"score_spread":0.08712500206081963,"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."}}