{"id":"W4372050163","doi":"10.1071/en23006","title":"Determination of inorganic As, DMA and MMA in marine and terrestrial tissue samples: a consensus extraction approach","year":2023,"lang":"en","type":"article","venue":"Environmental Chemistry","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Icelandic Centre for Research","keywords":"Certified reference materials; Extraction (chemistry); Context (archaeology); Environmental chemistry; Analyte; Inorganic arsenic; Matrix (chemical analysis); Arsenic; Complex matrix; Environmental science; Calibration; Sample preparation; Chemistry; Biochemical engineering; Chromatography; Detection limit; Biology; Mathematics; Statistics","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":[0.0001800173,0.0001941171,0.0002367946,0.00002812979,0.0000372984,0.00001445562,0.00008857777,0.0002045941,0.0007546147],"category_scores_gemma":[0.0001541004,0.0002197226,0.00002557281,0.00008891269,0.0002201252,0.00003917558,0.000216892,0.0002220904,0.0000077487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001684792,"about_ca_system_score_gemma":0.00002336796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004295111,"about_ca_topic_score_gemma":0.000001081033,"domain_scores_codex":[0.9987851,0.00001621831,0.0003561744,0.0004062846,0.0002225129,0.000213698],"domain_scores_gemma":[0.9993989,0.0001737628,0.0001244356,0.0001912919,0.000002415827,0.0001091664],"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":[0.00004829312,0.00007864799,0.02780039,0.0002818313,0.00001290303,0.00002811942,0.000120502,0.000001242446,0.9628596,0.000001087838,0.000007682168,0.008759657],"study_design_scores_gemma":[0.0009014564,0.000009357121,0.01294325,0.00003235845,0.00002504275,0.0001452805,0.0009579607,0.0006025687,0.983633,0.0001895347,0.0003379807,0.0002222249],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955142,0.00005876524,0.0001091314,0.00003791996,0.00001231685,0.00006435413,0.00002412413,0.00003625232,0.00414292],"genre_scores_gemma":[0.9904067,0.0001114156,0.007533423,0.000006122254,0.00006308026,0.00002014579,0.0002201426,0.00002394554,0.001615019],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02077334,"threshold_uncertainty_score":0.8960027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02068600083565555,"score_gpt":0.2683365250884143,"score_spread":0.2476505242527588,"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."}}