{"id":"W2954362452","doi":"10.3390/rs11111359","title":"Estimation of Vegetation Latent Heat Flux over Three Forest Sites in ChinaFLUX using Satellite Microwave Vegetation Water Content Index","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"People's Government of Jilin Province; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Environmental science; Latent heat; Evapotranspiration; Satellite; Vegetation (pathology); Water content; Remote sensing; Microwave; Flux (metallurgy); Water balance; Enhanced vegetation index; Atmosphere (unit); Scale (ratio); Leaf area index; Water cycle; Atmospheric sciences; Meteorology; Normalized Difference Vegetation Index; Vegetation Index; Geography; Geology; Computer science","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":[0.0003809992,0.0005802357,0.0003307917,0.0009750131,0.0003616428,0.0004472607,0.0004675609,0.0004660571,0.0005010563],"category_scores_gemma":[0.0002527278,0.0001903888,0.0004411557,0.0008446682,0.0002490126,0.0004374282,0.0002439486,0.0001709567,0.00008624654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006095222,"about_ca_system_score_gemma":0.0004750874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05666796,"about_ca_topic_score_gemma":0.07316536,"domain_scores_codex":[0.999891,0.00001194662,0.000006615632,0.00004250654,0.00001993401,0.0000278774],"domain_scores_gemma":[0.9998477,0.00003187136,0.00002579725,0.00001815086,0.00003880989,0.00003770228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000530421,0.0004576537,0.891983,0.0001400227,0.0002512272,0.0009189437,0.0005850684,0.03745625,0.03255387,0.0002637472,0.001131537,0.03372832],"study_design_scores_gemma":[0.00004082315,0.00004321548,0.9403445,0.000008140946,0.00005615694,0.0000521904,0.0001750389,0.05622872,0.002712666,0.0000391619,0.0002771829,0.00002224572],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991455,0.00003828609,0.0002575413,0.00001141779,0.000002762836,0.000005930281,0.0003118031,0.00002181877,0.0002049945],"genre_scores_gemma":[0.9979457,0.0000302824,0.0007463917,0.000007484027,0.000003952514,0.00001085404,0.001089786,0.000003903241,0.0001615632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05666796,"threshold_uncertainty_score":0.1126762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01618709638329983,"score_gpt":0.214777056150879,"score_spread":0.1985899597675792,"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."}}