{"id":"W2114980643","doi":"10.5194/amt-2-813-2009","title":"A laboratory flow reactor with gas particle separation and on-line MS/MS for product identification in atmospherically important reactions","year":2009,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Chemistry; Chemical ionization; Analytical Chemistry (journal); Particle (ecology); Mass spectrometry; Particulates; Atmospheric-pressure chemical ionization; Quadrupole mass analyzer; Diffusion; Chromatography; Ionization; Ion; Organic chemistry","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":[0.0009450099,0.0002511972,0.0002568076,0.000001607973,0.0001498171,0.00009302836,0.0001366605,0.00008339065,0.0001102786],"category_scores_gemma":[0.0001823864,0.0002027236,0.00004054028,0.0004663642,0.00006868097,0.0003370179,0.000004315546,0.0001582657,0.000005564251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005445545,"about_ca_system_score_gemma":0.000123177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002362062,"about_ca_topic_score_gemma":0.0004104963,"domain_scores_codex":[0.998044,0.00006017132,0.0005426288,0.0005458089,0.0004683009,0.0003391227],"domain_scores_gemma":[0.9989775,0.00005299017,0.0002583421,0.0003570698,0.0002224363,0.0001316539],"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":[0.002286779,0.001296005,0.2175089,0.0002534361,0.000140076,0.00005146716,0.0007546992,0.005253898,0.4411151,0.0003293309,0.004607751,0.3264026],"study_design_scores_gemma":[0.001971021,0.004749373,0.3086776,0.0005151091,0.0001441159,0.0000411373,0.0002487041,0.08111195,0.5706438,0.002246355,0.0281008,0.00154998],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9742084,0.0007634756,0.01970032,0.001763328,0.00008900261,0.001900892,0.00003174226,0.0004639143,0.001078922],"genre_scores_gemma":[0.9374055,0.0001740464,0.06180357,0.0002595429,0.0001218236,0.00007495858,0.0000496541,0.000009955869,0.0001009515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3248526,"threshold_uncertainty_score":0.8266829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02593751884398814,"score_gpt":0.2542170060083719,"score_spread":0.2282794871643838,"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."}}