{"id":"W4409705200","doi":"10.3390/separations12050105","title":"Optimizing Liquid Electron Ionization Interface to Boost LC-MS Instrumental Efficiency","year":2025,"lang":"en","type":"article","venue":"Separations","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver Island University","funders":"","keywords":"Ionization; Interface (matter); Electron; Chemistry; Analytical Chemistry (journal); Computer science; Materials science; Chromatography; Ion; Physics; Nuclear physics; Organic chemistry; Aqueous solution","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005378115,0.0001263477,0.0001094061,0.0001468385,0.0003048042,0.00008194864,0.0002190033,0.00007544915,0.001421703],"category_scores_gemma":[0.00003021568,0.0001391109,0.00004863617,0.0007211682,0.00002474165,0.00009530118,0.00008469381,0.000144013,0.00007501115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002180919,"about_ca_system_score_gemma":0.00007383295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003105815,"about_ca_topic_score_gemma":0.00004297074,"domain_scores_codex":[0.9991337,0.000008294615,0.0002401099,0.000282587,0.0001103056,0.0002249815],"domain_scores_gemma":[0.9994719,0.00002477223,0.00005145261,0.0003304707,0.00006058722,0.00006082037],"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":[0.00003307716,0.0002166152,0.0001130152,0.00002419149,0.00002593087,3.281992e-7,0.0003016526,0.002103876,0.8685111,0.1206732,0.007485914,0.0005111671],"study_design_scores_gemma":[0.0001548426,0.00008225912,0.00003879875,0.00004524283,0.00002223289,0.000002623854,0.0001127635,0.003180104,0.9292094,0.0007733565,0.06619829,0.0001800703],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4064684,0.0001155777,0.1795679,0.003170141,0.00007491976,0.0003616534,0.00004019722,0.0005351915,0.409666],"genre_scores_gemma":[0.9860123,0.00002005657,0.006014259,0.0001712924,0.00004606228,0.0001951136,0.00009844769,0.00001262964,0.007429864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5795438,"threshold_uncertainty_score":0.9994912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007485016835102486,"score_gpt":0.2976882847861037,"score_spread":0.2902032679510012,"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."}}