{"id":"W4405800233","doi":"10.3390/toxics13010006","title":"Global Assessment of Emerging Contaminant Removal in Wastewater Treatment Plants: In Silico Hazard Screening and Risk Evaluation","year":2024,"lang":"en","type":"article","venue":"Toxics","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"ARC Resources (Canada)","funders":"Ministero dell’Istruzione, dell’Università e della Ricerca","keywords":"Bioaccumulation; Hazardous waste; Quantitative structure–activity relationship; Environmental science; Environmental impact of pharmaceuticals and personal care products; Environmental chemistry; Bioconcentration; Hazard analysis; Risk assessment; Wastewater; Aquatic environment; Hazard; Biochemical engineering; Chemistry; Computer science; Environmental engineering; Biology; Ecology; Engineering","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":[0.0004807093,0.0001135423,0.0001408029,0.00003194277,0.00002417031,0.00001877203,0.00005045979,0.00004061486,0.0002236311],"category_scores_gemma":[0.00001228636,0.00008721843,0.00002636678,0.0001250353,0.00008519585,0.0001507614,0.0000884338,0.00007401058,0.00001277176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004906437,"about_ca_system_score_gemma":0.00001047851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005951363,"about_ca_topic_score_gemma":0.0007065086,"domain_scores_codex":[0.9989583,0.00009242717,0.0002276477,0.0002379676,0.0002707256,0.0002129675],"domain_scores_gemma":[0.9997731,0.00003726887,0.00003349313,0.00008551679,7.191516e-7,0.00006989795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002284573,0.0001808414,0.8087991,0.00001829162,0.00001811954,0.000102531,0.0004343986,0.007690522,0.02168472,0.00003413657,0.00001649612,0.160998],"study_design_scores_gemma":[0.0007418316,0.0001228363,0.7144414,0.0001226916,0.00003174864,0.00002248444,0.0002180864,0.2764048,0.007102455,0.0001786738,0.0005053392,0.0001076013],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970418,0.0003150698,0.0001242018,0.0001393984,0.00005869337,0.0002194732,0.00003190492,0.000009543261,0.002059909],"genre_scores_gemma":[0.9982828,0.0004653894,0.001154464,0.00002319244,0.000009618736,0.000003447659,0.000007404992,0.000005692458,0.00004795128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2687143,"threshold_uncertainty_score":0.3556665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03381573491650469,"score_gpt":0.3492475843839855,"score_spread":0.3154318494674808,"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."}}