{"id":"W2346170627","doi":"10.1002/2015jd024447","title":"An idealized stratospheric model useful for understanding differences between long‐lived trace gas measurements and global chemistry‐climate model output","year":2016,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Atmospheric Ozone and Climate","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Environment and Climate Change Canada","funders":"Canadian Space Agency; National Oceanic and Atmospheric Administration","keywords":"Trace gas; Stratosphere; Replicate; Atmospheric model; TRACE (psycholinguistics); Environmental science; Climate model; Atmospheric sciences; Extratropical cyclone; General Circulation Model; Tropics; Climatology; Meteorology; Geology; Climate change; Mathematics; Physics; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.000661838,0.0005554143,0.0005215895,0.0003418868,0.0005352828,0.0009924567,0.001331967,0.000968122,0.001814535],"category_scores_gemma":[0.002077625,0.0004928107,0.0008020087,0.0004572928,0.0006147223,0.0009645008,0.0007414243,0.0008727763,0.000215223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001482055,"about_ca_system_score_gemma":0.002209901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04325385,"about_ca_topic_score_gemma":0.01591424,"domain_scores_codex":[0.9997496,0.0001047799,0.00001257269,0.00005510031,0.00003974605,0.00003828761],"domain_scores_gemma":[0.9993926,0.0002500525,0.00008276417,0.00008981901,0.0001353207,0.00004930461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002824287,0.0000134545,0.001137193,0.000006108677,0.00001590426,0.00002167793,0.00001155307,0.996278,0.0007115169,0.00121115,0.0001096019,0.0004555425],"study_design_scores_gemma":[0.00001745806,0.00000982577,0.0002982083,0.000001041037,0.000005394511,0.0000030128,0.000005170563,0.9989366,0.0001672199,0.0003986797,0.0001527754,0.00000459629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7942526,0.000189134,0.1853287,0.0008576871,0.0001692635,0.0001443399,0.003306475,0.00160219,0.01414963],"genre_scores_gemma":[0.9859407,0.0000494307,0.01196971,0.0000700784,0.00001587191,0.0001056758,0.0006221968,0.00009900297,0.001127345],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04325385,"threshold_uncertainty_score":0.08600414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.176419508587402,"score_gpt":0.3471162862734344,"score_spread":0.1706967776860324,"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."}}