{"id":"W2995600715","doi":"10.1007/s12161-019-01666-6","title":"Potential of Recent Ambient Ionization Techniques for Future Food Contaminant Analysis Using (Trans)Portable Mass Spectrometry","year":2019,"lang":"en","type":"article","venue":"Food Analytical Methods","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Ministerie van Landbouw, Natuur en Voedselkwaliteit; Wageningen University and Research; University of Waterloo","keywords":"DART ion source; Mass spectrometry; Ambient ionization; Chemistry; Orbitrap; Atmospheric-pressure chemical ionization; Analytical Chemistry (journal); Triple quadrupole mass spectrometer; Ionization; Miniaturization; Sample preparation; Chromatography; Tandem mass spectrometry; Chemical ionization; Selected reaction monitoring; Electron ionization; Nanotechnology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001797003,0.001245255,0.0003563401,0.0007066015,0.0002429716,0.000766272,0.0007986581,0.0007582107,0.001439412],"category_scores_gemma":[0.001113069,0.0003301556,0.0005956764,0.0006401501,0.0004865348,0.000896419,0.000862928,0.001055612,0.0007138692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002444844,"about_ca_system_score_gemma":0.0004115981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003833033,"about_ca_topic_score_gemma":0.0007776746,"domain_scores_codex":[0.9990333,0.0002199038,0.00005385963,0.0002350382,0.0003995294,0.00005838244],"domain_scores_gemma":[0.9993887,0.0001578412,0.0001280839,0.00007089024,0.0002142154,0.0000402675],"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":[0.00009484075,0.00002325638,0.0008621488,0.0001537737,0.00002793349,0.00007807065,0.00002931421,0.0001421243,0.9821193,0.0002214312,0.000168546,0.01607924],"study_design_scores_gemma":[0.00001773339,0.000828206,0.004235467,0.00004372846,0.0001026493,0.00125422,0.00008407927,0.00392839,0.9785493,0.0003626706,0.01054905,0.00004446213],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5637461,0.02011668,0.4039293,0.001327873,0.0005152225,0.0004575743,0.001078915,0.002073342,0.00675505],"genre_scores_gemma":[0.669583,0.009453913,0.3134678,0.0008832613,0.0002499388,0.0002511096,0.001150742,0.0002472619,0.004713074],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001797003,"threshold_uncertainty_score":0.009503603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02428693128387916,"score_gpt":0.3436491634965201,"score_spread":0.319362232212641,"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."}}