{"id":"W2037451317","doi":"10.1021/ac1013892","title":"In Situ Characterization of Cloud Condensation Nuclei, Interstitial, and Background Particles Using the Single Particle Mass Spectrometer, SPLAT II","year":2010,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biological and Environmental Research; Pacific Northwest National Laboratory; Basic Energy Sciences; National Research Council Canada; Battelle; U.S. Department of Energy","keywords":"Cloud condensation nuclei; Aerosol; Chemistry; Particle (ecology); Sulfate; Condensation; Characterization (materials science); Particle size; Spectrometer; Mass spectrometry; Chemical physics; Nanotechnology; Meteorology; Optics; Physical chemistry; Materials science; Physics; Chromatography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002410937,0.0002978199,0.0002599707,0.0004372731,0.0003431603,0.0004031597,0.0002965115,0.0002527035,0.0007428437],"category_scores_gemma":[0.000289715,0.0001371328,0.0001444549,0.0002239308,0.0001558478,0.0003449068,0.0002282996,0.0002026085,0.0002217047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002421317,"about_ca_system_score_gemma":0.0002258981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003194736,"about_ca_topic_score_gemma":0.005652118,"domain_scores_codex":[0.9998052,0.00001001039,0.000008500084,0.00007626016,0.00008176966,0.00001843107],"domain_scores_gemma":[0.999892,0.00002545658,0.00001696252,0.000009117112,0.00004044504,0.00001611341],"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":[0.00007671605,0.00003688403,0.02006392,0.0000397299,0.00001555862,0.00004737051,0.000062062,0.0004454228,0.9711016,0.0001320198,0.0001298636,0.007848834],"study_design_scores_gemma":[0.00002425836,0.0002848335,0.08637467,0.000008052756,0.00003644498,0.0002611504,0.00011583,0.02616349,0.8844009,0.0002369656,0.002076927,0.00001635763],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9780177,0.0001732772,0.01829648,0.00003701855,0.00001852933,0.00006572941,0.001101221,0.0001971073,0.002092918],"genre_scores_gemma":[0.9633424,0.000178452,0.03353614,0.00005915896,0.0000199279,0.00004979114,0.0013003,0.00006441744,0.001449402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003194736,"threshold_uncertainty_score":0.006352305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02416332160308308,"score_gpt":0.2352317730873357,"score_spread":0.2110684514842526,"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."}}