{"id":"W1971869295","doi":"10.1016/j.aca.2006.08.049","title":"The coupling of solid-phase microextraction/surface enhanced laser desorption/ionization to ion mobility spectrometry for drug analysis","year":2006,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"Smiths Detection (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Solid-phase microextraction; Ion-mobility spectrometry; Mass spectrometry; Sample preparation; Chromatography; Analytical Chemistry (journal); Fiber; Analyte; Surface-enhanced laser desorption/ionization; Desorption; Ambient ionization; Extraction (chemistry); Ionization; Ion; Chemical ionization; Sample preparation in mass spectrometry; Adsorption; Gas chromatography–mass spectrometry; Electrospray ionization","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":[0.0009466526,0.0008965796,0.0009239044,0.0008698119,0.0005001122,0.000667331,0.0008899154,0.001023342,0.001556714],"category_scores_gemma":[0.001045766,0.0006059937,0.0007002965,0.0006908795,0.0007128997,0.0008822104,0.0008909361,0.001570042,0.001637904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005797092,"about_ca_system_score_gemma":0.001132801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007852693,"about_ca_topic_score_gemma":0.002259375,"domain_scores_codex":[0.9991424,0.0001759087,0.00003648697,0.0002293877,0.0003445465,0.00007120629],"domain_scores_gemma":[0.9994702,0.0002298784,0.00007154207,0.00005514283,0.0001272127,0.00004603096],"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":[0.00007874725,0.0000359649,0.0001459741,0.000102694,0.00002113481,0.00003605456,0.00002199367,0.00008408679,0.9884555,0.0002644461,0.0001346492,0.01061883],"study_design_scores_gemma":[0.000007041254,0.0001753822,0.0007811892,0.00000704343,0.00001682453,0.0002193156,0.00001109506,0.000964719,0.9945366,0.0001814689,0.003086569,0.00001276149],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3067681,0.01798895,0.6617402,0.001386574,0.001061138,0.001270621,0.001299645,0.001487132,0.006997712],"genre_scores_gemma":[0.5343854,0.0122266,0.4341779,0.001349588,0.0003296224,0.0008055783,0.001450364,0.0002828432,0.0149921],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001556714,"threshold_uncertainty_score":0.005207777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008823170605954167,"score_gpt":0.3044238354909712,"score_spread":0.2956006648850171,"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."}}