{"id":"W1555140969","doi":"10.1002/9780813823621.ch20","title":"Solid‐Phase Microextraction for Drug Analysis","year":2010,"lang":"en","type":"other","venue":"","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Derivatization; Solid-phase microextraction; Chromatography; Drug; Computer science; Chemistry; Pharmacology; Medicine; High-performance liquid chromatography; Gas chromatography–mass spectrometry; Mass spectrometry","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001799844,0.0003575047,0.0006035063,0.0001820761,0.00004103096,0.00004122156,0.0002862366,0.0007035468,0.1336894],"category_scores_gemma":[0.0001254228,0.000331049,0.0004901704,0.0002094368,0.0000583535,0.00001660022,0.00004864532,0.0004368556,0.0001154501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008600037,"about_ca_system_score_gemma":0.00008338848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009119255,"about_ca_topic_score_gemma":0.0002305891,"domain_scores_codex":[0.9985232,0.000007569279,0.0003503239,0.0005864431,0.0002111947,0.0003212298],"domain_scores_gemma":[0.998739,0.0001530851,0.0002763123,0.0005988993,0.00005850473,0.0001742164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000032433,0.000307056,0.00003386012,0.0006119735,0.004072305,0.000007741383,0.00001836014,5.785325e-7,0.350668,0.0000767969,0.6378381,0.006332777],"study_design_scores_gemma":[0.0003933476,0.000001548278,4.173744e-7,0.00001987619,0.001391841,0.000001621813,0.00002050029,0.0002640157,0.2561094,0.0000615008,0.7414037,0.0003322556],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00001804314,0.00006391121,0.2430062,0.00008222152,0.00009553846,0.00008369118,0.0001564105,0.0002795869,0.7562144],"genre_scores_gemma":[0.0001241425,0.00002443195,0.150959,0.00005014531,0.0005406537,0.00005784768,0.0005801641,0.0003014441,0.8473622],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.133574,"threshold_uncertainty_score":0.9999142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02089279990959204,"score_gpt":0.3861581389416801,"score_spread":0.365265339032088,"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."}}