{"id":"W3103895141","doi":"10.1021/jasms.0c00303","title":"Direct Coupling of Bio-SPME to Liquid Electron Ionization-MS/MS via a Modified Microfluidic Open Interface","year":2020,"lang":"en","type":"article","venue":"Journal of the American Society for Mass Spectrometry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Vancouver Island University","funders":"Natural Sciences and Engineering Research Council of Canada; Agilent Technologies","keywords":"Chemistry; Chromatography; Mass spectrometry; Analytical Chemistry (journal); Microfluidics; Triple quadrupole mass spectrometer; Sample preparation; Detection limit; Ionization; Desorption; Direct coupling; Tandem mass spectrometry; Selected reaction monitoring; Nanotechnology; Adsorption; Materials science","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":[0.0004129349,0.0002766178,0.0007893572,0.00009699786,0.0001959839,0.00009422109,0.001863141,0.00008484738,0.0003810985],"category_scores_gemma":[0.0001744178,0.0002230484,0.0009567477,0.002116403,0.0001651856,0.0001412577,0.0003579398,0.0004730081,0.000003499737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004298388,"about_ca_system_score_gemma":0.0001698248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006920043,"about_ca_topic_score_gemma":7.54132e-7,"domain_scores_codex":[0.9979688,0.00001588559,0.000814679,0.0003492502,0.0004234297,0.0004279572],"domain_scores_gemma":[0.9972624,0.0001827039,0.001534562,0.0004905367,0.000299658,0.0002301568],"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":[0.0003682857,0.0001250464,0.0002474748,0.0000773027,0.0003555172,4.551652e-7,0.0001831347,0.0002111651,0.9845082,0.0003999656,0.01341294,0.0001104713],"study_design_scores_gemma":[0.0004850918,0.00100857,0.00007177486,0.00007634721,0.0001683179,0.00001726459,0.0004082961,0.001051564,0.9882597,0.0005462527,0.00764895,0.0002578088],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7508492,0.0009127195,0.2339288,0.01101001,0.000066184,0.0005957184,0.0001059478,0.00008654186,0.002444823],"genre_scores_gemma":[0.9627576,0.0005005231,0.03523292,0.0008274383,0.0003269234,0.00003337206,0.000005913056,0.00006749703,0.0002477645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2119084,"threshold_uncertainty_score":0.909565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01698688908490472,"score_gpt":0.2894868696547566,"score_spread":0.2724999805698519,"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."}}