{"id":"W3010687662","doi":"10.1016/j.talanta.2020.120923","title":"Improved extraction of multiclass cyanotoxins from soil and sensitive quantification with on-line purification liquid chromatography tandem mass spectrometry","year":2020,"lang":"en","type":"article","venue":"Talanta","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Génome Québec; Fonds Québécois de la Recherche sur la Nature et les Technologies; Genome Canada; China Scholarship Council; Canada Foundation for Innovation","keywords":"Chemistry; Cylindrospermopsin; Ammonium acetate; Soil water; Environmental chemistry; Chromatography; Extraction (chemistry); Microcystin; Solid phase extraction; Soil test; Matrix (chemical analysis); Cyanobacteria; High-performance liquid chromatography; Soil science; Environmental science","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":[],"consensus_categories":[],"category_scores_codex":[0.00009961855,0.0001268869,0.0001795399,0.00003110107,0.00005918401,0.00001755762,0.00007120916,0.00006696065,0.00006211283],"category_scores_gemma":[0.00001882016,0.0001071519,0.00002961216,0.0002247227,0.00008792707,0.0001192531,0.00001951065,0.0001032618,0.00003514035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003841671,"about_ca_system_score_gemma":0.000007515845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003776727,"about_ca_topic_score_gemma":0.0003248959,"domain_scores_codex":[0.9990979,0.00004326024,0.0002290405,0.0003183616,0.0001908334,0.0001205632],"domain_scores_gemma":[0.9993965,0.0000829186,0.0002377258,0.0001859752,0.00001174434,0.00008510931],"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":[0.000219367,0.00006463264,0.01392699,0.0000182904,0.00003048493,0.000003652231,0.0004748182,0.0005376849,0.9843372,0.0001363315,0.00002415611,0.0002264306],"study_design_scores_gemma":[0.0009042299,0.001109018,0.0786474,0.00006270063,0.00006332129,0.00001314576,0.0006779555,0.3293416,0.5886981,0.00005527723,0.0001233322,0.0003039006],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9752507,0.00001159253,0.02324796,0.0002508625,0.00003720379,0.0002804198,0.00009871412,0.00003606298,0.0007864959],"genre_scores_gemma":[0.9982796,0.00003279258,0.001433619,0.0000646252,0.000046287,0.000008501589,0.000100472,0.00001431751,0.00001980438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3956391,"threshold_uncertainty_score":0.4369529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01413431414781174,"score_gpt":0.225980712346214,"score_spread":0.2118463981984022,"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."}}