{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005469962,0.001079276,0.0007806471,0.0007883286,0.0003329682,0.0006039635,0.0005142302,0.0007912495,0.0007077368],"category_scores_gemma":[0.0007020061,0.0003244909,0.0003705434,0.0006321541,0.0003021496,0.0005045915,0.0007368364,0.0009335304,0.0007055874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004533691,"about_ca_system_score_gemma":0.0008181939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001718332,"about_ca_topic_score_gemma":0.003674879,"domain_scores_codex":[0.9992151,0.0001397231,0.00004472023,0.000172427,0.0003034981,0.0001244905],"domain_scores_gemma":[0.9995866,0.00007507756,0.00006595859,0.00004317715,0.0001812142,0.00004780095],"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":[0.00002990078,0.0000230277,0.0002478511,0.00002076582,0.000006901153,0.00001536789,0.000005689367,0.00007750305,0.9949247,0.00001834408,0.00004209606,0.004587855],"study_design_scores_gemma":[0.000009015204,0.0001024863,0.002543906,0.000006784828,0.00001728207,0.0001557305,0.00001719644,0.002625826,0.9926535,0.00007151422,0.001782545,0.00001420313],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7148841,0.003640373,0.2741196,0.0004521934,0.0001287089,0.0003941214,0.002130612,0.001307234,0.002943077],"genre_scores_gemma":[0.7881488,0.002835957,0.1967869,0.0004219269,0.00005509158,0.0003157857,0.002896447,0.000247807,0.008291208],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001718332,"threshold_uncertainty_score":0.003416657,"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."}}