{"id":"W2781962742","doi":"10.5194/amt-11-3081-2018","title":"Identification of organic hydroperoxides and peroxy acids using atmospheric pressure chemical ionization–tandem mass spectrometry (APCI-MS/MS): application to secondary organic aerosol","year":2018,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Alfred P. Sloan Foundation; National Science Foundation","keywords":"Chemistry; Atmospheric-pressure chemical ionization; Mass spectrometry; Cumene hydroperoxide; Chemical ionization; Peroxide; Isoprene; Photochemistry; Tandem mass spectrometry; Ozonolysis; Organic peroxide; Chromatography; Organic chemistry; Ionization; Ion","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":[0.0003701354,0.0007147442,0.0002219385,0.0006793222,0.0002716242,0.000312798,0.0003651797,0.0005406906,0.000901239],"category_scores_gemma":[0.0004267797,0.0001739328,0.0002456612,0.0002869479,0.0002842595,0.0003654416,0.0004337276,0.0004532431,0.000573399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001750315,"about_ca_system_score_gemma":0.0003159287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002609673,"about_ca_topic_score_gemma":0.0004276147,"domain_scores_codex":[0.9997669,0.00002394941,0.00001575865,0.00007483166,0.00009508141,0.00002350875],"domain_scores_gemma":[0.9997578,0.00003513019,0.00007579918,0.00001792708,0.00007626085,0.00003708694],"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":[0.00002432705,0.00001175495,0.000494311,0.0000508516,0.000009050752,0.00006565622,0.000009950948,0.00001719307,0.9962217,0.00002266743,0.00002832951,0.003044224],"study_design_scores_gemma":[0.00000774818,0.0002649683,0.006304007,0.000009698664,0.00002239685,0.000718459,0.00003253894,0.001225211,0.9891008,0.00007751591,0.002226333,0.00001038567],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9080067,0.007935737,0.07822648,0.000242512,0.00020445,0.0003954347,0.0009419221,0.0005220465,0.003524696],"genre_scores_gemma":[0.9099139,0.005039419,0.08074997,0.0002679121,0.0001451768,0.0002339593,0.0006028478,0.00006152465,0.002985315],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.000901239,"threshold_uncertainty_score":0.003014982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01185978340678781,"score_gpt":0.2162961581746011,"score_spread":0.2044363747678133,"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."}}