{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001500685,0.001233683,0.0007340729,0.0007822399,0.0003972483,0.0009869754,0.001211024,0.001014432,0.001849727],"category_scores_gemma":[0.001964703,0.0004440226,0.0004317739,0.0004894191,0.0003890703,0.001229777,0.0007882929,0.0008020813,0.001136769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00036425,"about_ca_system_score_gemma":0.000447824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004840518,"about_ca_topic_score_gemma":0.0007757521,"domain_scores_codex":[0.9989246,0.0001455574,0.00008161408,0.0002832272,0.0004122727,0.0001527986],"domain_scores_gemma":[0.9993179,0.0002588762,0.00004914948,0.00003953221,0.0002997875,0.00003462751],"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":[0.00013306,0.00005344431,0.0003631428,0.0002029323,0.00002089415,0.00009666345,0.00003698175,0.0002827708,0.9896417,0.000258837,0.0003433684,0.008566231],"study_design_scores_gemma":[0.00003644621,0.0001523398,0.001446486,0.00001475293,0.00004530945,0.0001625617,0.00003097176,0.006644842,0.9865531,0.0001302516,0.004752266,0.00003067613],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6647372,0.01052388,0.3121665,0.0006632227,0.0006276752,0.001012344,0.0006875293,0.003917768,0.00566389],"genre_scores_gemma":[0.6519127,0.004761083,0.3359137,0.0007053288,0.0001422768,0.001127111,0.0008229286,0.0005996155,0.004015188],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001849727,"threshold_uncertainty_score":0.007936478,"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."}}