{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002744457,0.0002766181,0.0002999498,0.0003497503,0.0004556784,0.000135176,0.0002265842,0.0001262148,0.01348675],"category_scores_gemma":[0.00005503661,0.0002133357,0.00009113478,0.001983497,0.0001026669,0.0002016302,0.00007441225,0.0003051919,0.0001371448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004702337,"about_ca_system_score_gemma":0.000008628667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008278107,"about_ca_topic_score_gemma":0.00006935499,"domain_scores_codex":[0.9981647,0.00006054059,0.0004100504,0.0006430557,0.000361987,0.0003596847],"domain_scores_gemma":[0.9984716,0.0000697819,0.0002667155,0.0008790314,0.0001587867,0.000154155],"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":[0.0001483052,0.0004480258,0.001174954,0.0000427113,0.0001132287,0.000003195801,0.00004579456,0.00009539126,0.990774,0.0003948091,0.006122583,0.0006370411],"study_design_scores_gemma":[0.00126554,0.0002776581,0.003321656,0.00003165563,0.0003869661,0.0002268005,0.0002276207,0.006122117,0.9704231,0.0005088758,0.01671247,0.0004955136],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.949564,0.0002190343,0.04186215,0.004397289,0.00003980993,0.0004127095,0.00005363679,0.0003384515,0.003112945],"genre_scores_gemma":[0.9866288,0.0002135472,0.004413419,0.00005095397,0.0006614944,0.00009617893,0.0001500734,0.00003982746,0.007745694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03744873,"threshold_uncertainty_score":0.9874151,"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."}}