{"id":"W2089156212","doi":"10.1016/j.foodchem.2008.08.001","title":"Sequential extraction combined with HPLC–ICP-MS for As speciation in dry seafood products","year":2008,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Research Council Canada; National Natural Science Foundation of China","keywords":"Arsenobetaine; Arsenite; Arsenate; Arsenic; Chemistry; Chromatography; High-performance liquid chromatography; Extraction (chemistry); Genetic algorithm; Environmental chemistry; Biology; Organic chemistry; Ecology","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":[0.00007902406,0.00009153535,0.00007853819,0.00001183706,0.00008584619,0.000009454292,0.00006698724,0.00006657863,0.0005620818],"category_scores_gemma":[0.00004975304,0.00009233253,0.00002203841,0.0001366625,0.00005807867,0.0001881888,0.00002125713,0.00008282312,0.00004170605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002171622,"about_ca_system_score_gemma":0.00003662251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003206982,"about_ca_topic_score_gemma":0.00008551651,"domain_scores_codex":[0.9992517,0.000009509006,0.0001428829,0.0002511433,0.0002015655,0.0001431636],"domain_scores_gemma":[0.9996977,0.00001580876,0.00009022061,0.0001351573,0.00001866388,0.00004243387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003299266,0.0003623846,0.01221769,0.00006621824,0.00002032193,0.00001179163,0.001118047,0.0002384908,0.9778194,0.00009136839,0.001743507,0.005980886],"study_design_scores_gemma":[0.001450357,0.0002381047,0.02919193,0.0000135002,0.000009859286,0.00002916933,0.000121451,0.0005243675,0.9596748,0.0001920637,0.008379049,0.000175345],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816095,0.0000048565,0.0003826557,0.0003409414,0.00005773728,0.0003317907,0.000005612463,0.00003809843,0.01722887],"genre_scores_gemma":[0.9930156,0.000004253199,0.0008170876,0.00004383277,0.0001058354,0.00006838944,0.000101805,0.00001073389,0.005832474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01814456,"threshold_uncertainty_score":0.6154401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01270666215599357,"score_gpt":0.2194122129116329,"score_spread":0.2067055507556393,"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."}}