{"id":"W2036592541","doi":"10.1002/clen.200600016","title":"Speciation of Arsenic Using Chelation Solvent Extraction and High Performance Liquid Chromatography","year":2007,"lang":"en","type":"article","venue":"CLEAN - Soil Air Water","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Arsenic; Chemistry; Arsenate; High-performance liquid chromatography; Arsine; Chromatography; Extraction (chemistry); Detection limit; Solvent; Elution; Chelation; Genetic algorithm; Environmental chemistry; Inorganic chemistry; Organic 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003255588,0.0000802787,0.00007714714,0.00006659116,0.000101952,0.000006850696,0.00003706462,0.00005965523,0.0004484753],"category_scores_gemma":[0.000005506827,0.00006876297,0.0000310185,0.00009227311,0.00008551275,0.0003787484,0.00003801581,0.00005921201,0.00005789139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001086724,"about_ca_system_score_gemma":0.000003603421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001016611,"about_ca_topic_score_gemma":0.0001032115,"domain_scores_codex":[0.9992599,0.00001678846,0.0002329908,0.0001580425,0.0001810181,0.0001512427],"domain_scores_gemma":[0.9997261,0.00001147579,0.0001007274,0.00009701507,0.00001346049,0.00005121246],"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":[0.000130575,0.0001036739,0.06598697,0.00003357004,0.00001542906,0.000001996984,0.002016858,0.002545225,0.8541254,0.0001073636,0.00007556616,0.07485735],"study_design_scores_gemma":[0.0002359459,0.0000757098,0.5664843,0.00001399925,0.00001273757,0.000006930675,0.0001523141,0.005440312,0.4269639,0.00006419798,0.0004627043,0.00008687725],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994145,0.000005441531,0.0036501,0.00006501911,0.0001241897,0.0001118502,7.084494e-7,0.00002549631,0.00187218],"genre_scores_gemma":[0.9991545,0.00001750438,0.000465718,0.00005645767,0.00005602651,0.000001463732,0.00001842878,0.000008844604,0.0002210532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5004974,"threshold_uncertainty_score":0.491049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009328639956634717,"score_gpt":0.2216548839035689,"score_spread":0.2123262439469342,"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."}}