{"id":"W3016657135","doi":"","title":"Constraining ozone dry deposition using ozone and water vapor flux measurements","year":2019,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Ozone; Environmental science; Atmospheric sciences; Flux (metallurgy); Water vapor; Deposition (geology); Meteorology; Chemistry; Geography; Physics; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003806034,0.0007801726,0.000448543,0.0005163434,0.0004821509,0.0009563453,0.0003883903,0.0006704428,0.0008196447],"category_scores_gemma":[0.0006263885,0.0004884464,0.0004552228,0.0005706868,0.000180539,0.0005896788,0.0004473257,0.0004917821,0.0002874421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005095002,"about_ca_system_score_gemma":0.0007384475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0312701,"about_ca_topic_score_gemma":0.06034093,"domain_scores_codex":[0.9998506,0.00002014919,0.00000517716,0.00006263328,0.00002853027,0.00003290146],"domain_scores_gemma":[0.9998444,0.00006750615,0.00002083009,0.00002461996,0.00002117679,0.00002135086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009550435,0.000546766,0.3674807,0.0003279825,0.001163745,0.0003945538,0.0002353395,0.3104828,0.2459069,0.002793948,0.003830882,0.06588143],"study_design_scores_gemma":[0.0003111823,0.0002371411,0.4180091,0.00004351536,0.0003759748,0.00006497232,0.0001375868,0.5246112,0.04397237,0.002235627,0.009913572,0.00008772773],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9670104,0.0006726254,0.02147783,0.0001980201,0.00008557698,0.00004042825,0.003357218,0.0007564074,0.006401503],"genre_scores_gemma":[0.9908296,0.000161791,0.006910761,0.00004418755,0.0000230377,0.00001973609,0.001348614,0.00005263831,0.0006096277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0312701,"threshold_uncertainty_score":0.06217617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02738518184856112,"score_gpt":0.2324287067587894,"score_spread":0.2050435249102283,"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."}}