{"id":"W1934883515","doi":"10.1039/b204701b","title":"Solid phase microextraction with matrix assisted laser desorption/ionization introduction to mass spectrometry and ion mobility spectrometryPresented at Pittcon 2002.","year":2002,"lang":"en","type":"article","venue":"The Analyst","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mass spectrometry; Ion-mobility spectrometry; Chemistry; Ambient ionization; Ion source; Analytical Chemistry (journal); Solid-phase microextraction; MALDI imaging; Atmospheric-pressure laser ionization; Matrix-assisted laser desorption electrospray ionization; Sample preparation; Chromatography; Sample preparation in mass spectrometry; Ionization; Chemical ionization; Matrix-assisted laser desorption/ionization; Ion; Desorption; Thermal ionization mass spectrometry; Gas chromatography–mass spectrometry; Photoionization; 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":[0.001287117,0.001641555,0.001010994,0.001581559,0.0009214854,0.0005767557,0.001614515,0.001041089,0.02544358],"category_scores_gemma":[0.001196041,0.001301869,0.000607547,0.001214013,0.000629087,0.0009227585,0.001252754,0.00233637,0.01422855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001127181,"about_ca_system_score_gemma":0.001092403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00105553,"about_ca_topic_score_gemma":0.00314558,"domain_scores_codex":[0.9985851,0.0001829204,0.00007053051,0.0002583065,0.0008269923,0.00007627781],"domain_scores_gemma":[0.9993382,0.0001523047,0.00005619909,0.0001098672,0.0002505024,0.00009282593],"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":[0.0002034628,0.0001305987,0.0002025011,0.0003939308,0.00002395472,0.000279938,0.000053928,0.0001859366,0.917392,0.00176987,0.005063805,0.07430022],"study_design_scores_gemma":[0.0001231963,0.0006209734,0.002747936,0.00008242364,0.00004744001,0.00216149,0.0000426204,0.003745259,0.7657231,0.001832262,0.2227904,0.0000828368],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04363958,0.01560535,0.8951711,0.002386702,0.002446743,0.00310265,0.004363405,0.005600811,0.02768376],"genre_scores_gemma":[0.05633675,0.01533258,0.7812235,0.0007749262,0.0006799595,0.002750511,0.009594401,0.001100192,0.1322071],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02544358,"threshold_uncertainty_score":0.08511728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01331355926038664,"score_gpt":0.2988629632013173,"score_spread":0.2855494039409306,"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."}}