{"id":"W4386915163","doi":"10.1021/acs.est.3c02945","title":"Dynamic Source Distribution and Emission Inventory of a Persistent, Mobile, and Toxic (PMT) Substance, Melamine, in China","year":2023,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Melamine detection and toxicity","field":"Agricultural and Biological Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto Scarborough; Horizon 2020 Framework Programme; European Commission; State Key Joint Laboratory of Environmental Simulation and Pollution Control; University of Toronto; Peking University; Eidgenössische Materialprüfungs- und Forschungsanstalt","keywords":"Melamine; Environmental science; Waste management; Human health; Sewage treatment; Environmental engineering; Environmental health; Chemistry; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002600682,0.0000901253,0.000118798,0.00009040015,0.0001794844,0.00001002183,0.000159962,0.00009742984,0.00003671315],"category_scores_gemma":[0.00002482748,0.00004545679,0.00002920034,0.001163841,0.001003231,0.0001030669,0.0001533542,0.0001137802,0.000004657217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007984811,"about_ca_system_score_gemma":0.000002781671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002744968,"about_ca_topic_score_gemma":0.00008435669,"domain_scores_codex":[0.9990999,0.00001929244,0.0001472658,0.0003288494,0.0001687231,0.0002359665],"domain_scores_gemma":[0.9997973,0.00001674507,0.00006368873,0.00005660422,0.000002566905,0.00006305616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000006925882,0.00004710138,0.08757132,0.000003245769,0.00000139504,0.000002017025,0.0001025079,0.00001541391,0.8716267,0.00003185305,0.000005041027,0.04058643],"study_design_scores_gemma":[0.0001994308,0.0004250123,0.9507691,0.00001968556,0.000003905255,0.0000223052,0.002447647,0.005801533,0.03841235,0.0002460381,0.001506517,0.0001464571],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992038,0.0002313073,0.000004083667,0.0002409269,0.00003680256,0.0001560215,0.00001885237,0.00006031642,0.00004789572],"genre_scores_gemma":[0.9993737,0.0003397597,0.000008011581,0.000007572349,0.000004358814,0.0000167723,0.00001694555,6.660893e-7,0.0002321642],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8631978,"threshold_uncertainty_score":0.3696446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004692435633168631,"score_gpt":0.1907870318299983,"score_spread":0.1860945961968297,"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."}}