{"id":"W4377982003","doi":"10.1021/acs.analchem.3c01241","title":"Argentination: A Silver Bullet for Cannabinoid Separation by Differential Mobility Spectrometry","year":2023,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Cannabis and Cannabinoid Research","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Institute for Nanotechnology; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Hong Kong Government; Government of Canada; Government of Ontario; Ontario Centres of Excellence","keywords":"Chemistry; Cannabinoid; Cannabinol; Tandem mass spectrometry; Cannabidiol; Fragmentation (computing); Chromatography; Mass spectrometry; Synthetic cannabinoids; Ion-mobility spectrometry; Cannabis; Biochemistry","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":[0.002539342,0.001125408,0.001029071,0.001137565,0.0006185398,0.001668261,0.001612069,0.002546695,0.004906747],"category_scores_gemma":[0.00435424,0.0009446695,0.0008158618,0.0004772223,0.001627475,0.003263493,0.002984812,0.004919393,0.005425731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006201182,"about_ca_system_score_gemma":0.001174249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004408247,"about_ca_topic_score_gemma":0.0006733179,"domain_scores_codex":[0.9977372,0.0003901112,0.0001232758,0.000438076,0.001171726,0.0001397524],"domain_scores_gemma":[0.9984177,0.0004719066,0.0002227525,0.0002798375,0.0004182749,0.0001894732],"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":[0.000767996,0.0001910543,0.001334361,0.0007602345,0.0001342092,0.0008305463,0.000224588,0.0004420853,0.7308655,0.03013488,0.04229894,0.1920156],"study_design_scores_gemma":[0.0001649714,0.000802663,0.001276549,0.0004452104,0.00007938001,0.003588353,0.0001241874,0.009078226,0.5358663,0.01895236,0.4294193,0.0002024435],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03247462,0.03684501,0.8558993,0.02795441,0.008794113,0.0006161944,0.001143157,0.01434982,0.0219234],"genre_scores_gemma":[0.1334542,0.02998493,0.7647297,0.0188055,0.003220662,0.0009537774,0.002302669,0.002814902,0.0437337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004906747,"threshold_uncertainty_score":0.0164147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01738604935665581,"score_gpt":0.3344832034990265,"score_spread":0.3170971541423707,"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."}}