{"id":"W2795461796","doi":"10.5194/amt-2018-62","title":"Improving the Retrieval of XCO <sub>2</sub> from Total Carbon ColumnNetwork Solar Spectra","year":2018,"lang":"en","type":"article","venue":"","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Eurostars; Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; Jet Propulsion Laboratory; Australian Research Council; National Institute of Standards and Technology; Canadian Foundation for Climate and Atmospheric Sciences; Government of Canada; National Aeronautics and Space Administration; U.S. Department of Energy; California Institute of Technology; Nova Scotia Research Innovation Trust; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Voigt profile; Spectral line; Line (geometry); Physics; Computational physics; Absorption (acoustics); Environmental science; Optics; Mathematics; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002895212,0.0007530363,0.0002559132,0.0009608984,0.0002937938,0.000864154,0.0004779352,0.0004783433,0.0008224166],"category_scores_gemma":[0.0008157277,0.000300157,0.0003079879,0.0009708312,0.0001152242,0.0007872795,0.0003206778,0.0002761324,0.0004256199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000735283,"about_ca_system_score_gemma":0.0007845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04409375,"about_ca_topic_score_gemma":0.08016916,"domain_scores_codex":[0.9998719,0.00001379376,0.000006937606,0.00004751802,0.00004298988,0.00001693415],"domain_scores_gemma":[0.9997575,0.00004632869,0.00003120525,0.00002983556,0.0001186728,0.00001649783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005802151,0.0002782362,0.2705788,0.0004556015,0.000400817,0.0002891803,0.0002352702,0.07590862,0.4406842,0.0005554226,0.004896597,0.2051371],"study_design_scores_gemma":[0.0001020323,0.00006850235,0.3709714,0.00004236684,0.0001379761,0.0001019948,0.0001517856,0.5199044,0.09939402,0.0003205779,0.008736487,0.0000684614],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9713095,0.000525656,0.01955885,0.0001157508,0.00004676916,0.00006094429,0.003100245,0.001721489,0.003560775],"genre_scores_gemma":[0.9642901,0.0001887781,0.02871891,0.00006734153,0.00002869372,0.00003287979,0.005386647,0.0002004317,0.001086142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04409375,"threshold_uncertainty_score":0.0876742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003609164949273138,"score_gpt":0.1655668535053871,"score_spread":0.161957688556114,"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."}}