{"id":"W2590401746","doi":"10.1007/s00216-017-0256-3","title":"Leaner and greener analysis of cannabinoids","year":2017,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Cannabis and Cannabinoid Research","field":"Medicine","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"British Columbia Institute of Technology; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Repeatability; Detection limit; Cannabis; Cannabis sativa; Synthetic cannabinoids; Cannabidiol; Relative standard deviation; Biochemical engineering; Chromatography; Chemistry; Cannabinoid; Engineering; Medicine","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":[0.0002467315,0.0001861472,0.0007088384,0.0001236969,0.000173985,0.00009204123,0.0001556595,0.0001904552,0.0006404002],"category_scores_gemma":[0.0004231113,0.0001334374,0.0002833518,0.0002932593,0.001475823,0.00005827026,0.0002352329,0.0002726077,0.000002468933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002560597,"about_ca_system_score_gemma":0.00008644388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003641384,"about_ca_topic_score_gemma":0.00003087812,"domain_scores_codex":[0.9984378,0.00001055627,0.0003347334,0.0004522189,0.0004194764,0.0003452373],"domain_scores_gemma":[0.9984409,0.00002150996,0.00008726028,0.0006446207,0.0002035973,0.0006021296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007622826,0.0007427776,0.9048077,0.001297791,0.008053247,0.0004232205,0.00007087559,0.00000393631,0.03861253,0.006587405,0.01333836,0.02529981],"study_design_scores_gemma":[0.001438797,0.0001993978,0.919459,0.00006395759,0.00861041,0.00008483078,0.000120834,0.04189672,0.01395733,0.0002787117,0.01348798,0.00040204],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9639528,0.0003331179,0.00002242037,0.01717538,0.000009769772,0.00007395136,0.00003438788,0.00001400496,0.0183841],"genre_scores_gemma":[0.9697685,0.0001494517,0.00004130212,0.0001815239,0.00009204151,0.000003579556,0.00001833895,0.00001049309,0.02973483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04189279,"threshold_uncertainty_score":0.7011933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0201195175550712,"score_gpt":0.3187112884118068,"score_spread":0.2985917708567357,"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."}}