{"id":"W2992631516","doi":"10.1016/j.trac.2019.115770","title":"Arsenic speciation analysis: A review with an emphasis on chromatographic separations","year":2019,"lang":"en","type":"review","venue":"TrAC Trends in Analytical Chemistry","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":179,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Alberta Innovates; Alberta Health; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Arsenic; Genetic algorithm; Chemistry; Chromatography; Extraction (chemistry); Chromatographic separation; Sample preparation; Environmental chemistry; Solid phase extraction; Separation method; High-performance liquid chromatography; Biology; Ecology; Organic chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006649718,0.001413502,0.001594621,0.004108023,0.000448268,0.001178152,0.0009579838,0.001078153,0.005268184],"category_scores_gemma":[0.0008389354,0.0004261012,0.0008217421,0.00498334,0.000463303,0.001600351,0.0007355578,0.001238934,0.004135022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005567704,"about_ca_system_score_gemma":0.001494615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001934384,"about_ca_topic_score_gemma":0.002598847,"domain_scores_codex":[0.9996015,0.00005088228,0.00005292404,0.00008609832,0.0001788815,0.00002968781],"domain_scores_gemma":[0.9994697,0.0002122061,0.00007532709,0.0000167399,0.0002017006,0.00002430242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007208878,0.00009563437,0.0004112726,0.0518099,0.0001808946,0.0003241051,0.000104295,0.0006389524,0.008670809,0.003198823,0.04967632,0.8848169],"study_design_scores_gemma":[0.000005388063,0.00006580369,0.0007814494,0.002306512,0.0001623964,0.001125555,0.00006221891,0.0001199119,0.002244004,0.001184421,0.9919116,0.00003068265],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002955382,0.996381,0.0006286813,0.0003028197,0.0003097745,0.000012527,0.0001208042,0.00003356148,0.001915432],"genre_scores_gemma":[0.001035814,0.9966707,0.0006917618,0.0001920977,0.0001866203,0.00001208158,0.0001349764,0.000005837263,0.001070155],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005268184,"threshold_uncertainty_score":0.01762384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04652617798164149,"score_gpt":0.3638801879685931,"score_spread":0.3173540099869516,"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."}}