{"id":"W2886575915","doi":"10.1016/j.atmosenv.2018.08.030","title":"Passive sampling capabilities for ultra-trace quantitation of atmospheric nitric acid (HNO3) in remote environments","year":2018,"lang":"en","type":"article","venue":"Atmospheric Environment","topic":"Atmospheric Ozone and Climate","field":"Earth and Planetary Sciences","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; York University; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nitric acid; Sampling (signal processing); TRACE (psycholinguistics); Environmental science; Remote sensing; Environmental chemistry; Air monitoring; Trace gas; Chemistry; Meteorology; Atmospheric sciences; Geology; Environmental engineering; Computer science; Geography; Inorganic chemistry; Telecommunications","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00033465,0.0003424201,0.000449629,0.000003120441,0.0001866285,0.00002615723,0.000304458,0.0001592947,0.002661474],"category_scores_gemma":[0.00007356146,0.0003217282,0.0001431574,0.0003129409,0.0004228695,0.0002902476,0.00002224695,0.0001459133,0.0003192223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007364352,"about_ca_system_score_gemma":0.00003853953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001566702,"about_ca_topic_score_gemma":0.0003042567,"domain_scores_codex":[0.9975045,0.00009912206,0.0007114863,0.0006096458,0.0004375578,0.0006376568],"domain_scores_gemma":[0.9987387,0.0002979702,0.000364268,0.0004456431,0.00001537564,0.0001380132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005805747,0.0003812488,0.6192124,0.0002240572,0.0001771603,0.00001407319,0.003483862,0.06148799,0.006941005,0.0001564024,0.0002636699,0.3070775],"study_design_scores_gemma":[0.002016851,0.001820619,0.8918175,0.00009756484,0.0001265585,0.00001230144,0.004551444,0.06803153,0.003884571,0.00268601,0.02398904,0.0009660018],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9541109,0.001451084,0.04214139,0.0001274876,0.000234467,0.0007391117,0.0000468765,0.0000306352,0.001118111],"genre_scores_gemma":[0.8534018,0.0008287731,0.1449117,0.0001135799,0.0001080719,0.00001386789,0.00005843059,0.00002142602,0.0005423473],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3061115,"threshold_uncertainty_score":0.9999235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01535902011839124,"score_gpt":0.2284093643148564,"score_spread":0.2130503441964651,"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."}}