{"id":"W2581041139","doi":"","title":"Constraining CO Emissions Using MOPITT, TES, and OMI Satellite Retrievals","year":2014,"lang":"en","type":"article","venue":"2014 AGU Fall Meeting","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Environmental science; Satellite; Remote sensing; Meteorology; Climatology; Atmospheric sciences; Geography; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006194568,0.0002105027,0.0002199405,0.000007668211,0.0003335858,0.00004960535,0.0001622059,0.0001152625,0.0001557049],"category_scores_gemma":[0.0001296051,0.0001968335,0.00004749153,0.00009035485,0.0003981405,0.0001564097,0.0002286031,0.0001790799,0.0001235034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001226534,"about_ca_system_score_gemma":0.000006257751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007774124,"about_ca_topic_score_gemma":0.00004098517,"domain_scores_codex":[0.9985133,0.00009325323,0.0002915686,0.0004162775,0.000267276,0.0004183542],"domain_scores_gemma":[0.9992274,0.000159641,0.0001521406,0.0002315684,0.000002829273,0.0002264442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009456821,0.00002520122,0.9295444,0.00001146662,0.00001111194,0.000004799912,0.0004695913,0.01028927,0.02240365,0.00007086994,0.0001959116,0.0369643],"study_design_scores_gemma":[0.001897778,0.0002715558,0.3883872,0.0004314614,0.0001565139,0.0001891361,0.002708973,0.5226058,0.002697795,0.00135817,0.0773247,0.001970953],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9580408,0.0001986633,0.008424065,0.00006384047,0.00007789546,0.0001298715,0.000001766665,0.00006274691,0.03300038],"genre_scores_gemma":[0.9429108,0.0001932215,0.05598224,0.0003261932,0.00006605614,0.000002541935,0.000004131294,0.00003209865,0.0004827125],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5411572,"threshold_uncertainty_score":0.8026639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0123708229642967,"score_gpt":0.2318585708589135,"score_spread":0.2194877478946168,"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."}}