{"id":"W2060129091","doi":"10.1016/j.aca.2013.05.050","title":"A modified sequential extraction method for arsenic fractionation in sediments","year":2013,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Water Network","keywords":"Chemistry; Fractionation; Arsenic; Extraction (chemistry); Inductively coupled plasma mass spectrometry; Chloride; Sediment; Certified reference materials; Chromatography; Polyatomic ion; Mass spectrometry; Environmental chemistry; Detection limit; Ion; Geology","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":[0.0002582374,0.0001039282,0.000110977,0.00009263677,0.00008352567,0.00003686746,0.00009674885,0.00008340761,0.002814392],"category_scores_gemma":[0.00009782719,0.0001057418,0.00006902482,0.000204537,0.00003042208,0.0006142928,0.00003569428,0.0001059712,0.0002621074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002181545,"about_ca_system_score_gemma":0.00001580384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002680261,"about_ca_topic_score_gemma":0.0002511728,"domain_scores_codex":[0.9989831,0.00005829895,0.0002597911,0.0002848451,0.0002218191,0.0001921115],"domain_scores_gemma":[0.9995214,0.0001262606,0.000117064,0.0001468008,0.00002177904,0.00006672916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007061471,0.0004751047,0.003393572,0.00001455616,0.00006641584,0.000001197559,0.0007943658,0.0005274808,0.9164593,0.003432229,0.01504997,0.05971526],"study_design_scores_gemma":[0.001449134,0.000063799,0.1494819,0.0000124774,0.00006135753,0.000008849269,0.0002010932,0.8146676,0.01150717,0.007350755,0.0148851,0.0003107129],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.915355,0.000001110336,0.03968463,0.006969826,0.0003015772,0.001458178,0.000008742448,0.00008711292,0.03613384],"genre_scores_gemma":[0.9893202,0.000004867245,0.007874644,0.0003477918,0.00004309175,0.0001329689,0.00005770446,0.00001127312,0.002207442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.904952,"threshold_uncertainty_score":0.9980972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01901492546575438,"score_gpt":0.30223956341895,"score_spread":0.2832246379531956,"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."}}