{"id":"W3016447915","doi":"10.1029/2020ea001145","title":"Spatiotemporal Variations of Satellite Microwave Emissivity Difference Vegetation Index in China Under Clear and Cloudy Skies","year":2020,"lang":"en","type":"article","venue":"Earth and Space Science","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Normalized Difference Vegetation Index; Enhanced vegetation index; Vegetation (pathology); Environmental science; Subtropics; Deciduous; Physical geography; Climatology; Geography; Vegetation Index; Climate change; Geology; Ecology","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":[0.0002358397,0.0001996678,0.0001796994,0.0007728931,0.0002617295,0.0004244229,0.000176433,0.000160208,0.0003961699],"category_scores_gemma":[0.0002984571,0.0001137229,0.0002132909,0.000907545,0.0001845407,0.0002645245,0.000270859,0.0001114975,0.00005678735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005394231,"about_ca_system_score_gemma":0.0004086837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0525428,"about_ca_topic_score_gemma":0.06963886,"domain_scores_codex":[0.9998702,0.00001068093,0.00001060619,0.00004004718,0.00003285613,0.00003561391],"domain_scores_gemma":[0.9997451,0.00002650774,0.00008613779,0.00002270248,0.00006738759,0.00005219227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003857997,0.0000235733,0.9931304,0.00001453337,0.00005282161,0.0001310017,0.0001431674,0.0009818166,0.002327683,0.00005842979,0.0001759676,0.002922125],"study_design_scores_gemma":[0.000001100395,0.000005703624,0.9987142,0.000001093894,0.000007202902,0.00001297018,0.00006318297,0.0009892192,0.0001046348,0.000007646937,0.00009105031,0.000001885777],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999581,0.00002400781,0.00004090489,0.000007855357,0.000001175676,0.000001337925,0.0002048768,0.000002271351,0.0001363818],"genre_scores_gemma":[0.9993704,0.00001860043,0.00005307078,0.00000463072,0.00000188761,0.000002249471,0.0004174263,6.979955e-7,0.000131059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0525428,"threshold_uncertainty_score":0.1044739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007251317251705426,"score_gpt":0.1988890298597359,"score_spread":0.1916377126080304,"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."}}