{"id":"W2007042296","doi":"10.1021/ac000527u","title":"Speciation of Key Arsenic Metabolic Intermediates in Human Urine","year":2000,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":331,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Arsenic; Chemistry; Arsenite; Arsenate; Genetic algorithm; Urine; Metabolic pathway; Environmental chemistry; Arsenobetaine; Chromatography; Metabolism; Biochemistry; Organic chemistry; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009089895,0.00005906272,0.0001041029,0.00001181746,0.0000155674,0.000004532802,0.00008385924,0.00004391548,0.02930348],"category_scores_gemma":[0.00003542102,0.00005866595,0.0000403842,0.0001464233,0.00009308827,0.00006722421,0.00002626457,0.00007246873,0.0001313847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000495047,"about_ca_system_score_gemma":0.000004516105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006695135,"about_ca_topic_score_gemma":0.00004989949,"domain_scores_codex":[0.9993994,0.000009908073,0.000208869,0.0001356915,0.0001436057,0.0001025011],"domain_scores_gemma":[0.9997812,0.00001914829,0.00003778739,0.0001112196,0.000004378735,0.00004627468],"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":[0.00004653399,0.0007752062,0.3574821,0.00005750529,0.00004829211,0.0000237169,0.001274611,0.0006660333,0.5211878,0.001814584,0.001930546,0.1146931],"study_design_scores_gemma":[0.0008152405,0.00001964488,0.7220992,0.00002377167,0.00003731178,0.000004549915,0.000111906,0.009258407,0.2510327,0.001367431,0.01500604,0.0002237885],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8842251,0.00000999111,0.0000098633,0.0001137128,0.000005040228,0.00002895698,0.000001387985,0.000008628165,0.1155974],"genre_scores_gemma":[0.9916131,0.00001865595,0.00003995607,0.00004263554,0.00002898042,0.000002439155,0.00001865632,0.000003624145,0.008231949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3646172,"threshold_uncertainty_score":0.9715838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005428721724744017,"score_gpt":0.2299976780077471,"score_spread":0.2245689562830031,"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."}}