{"id":"W2560846357","doi":"10.1109/jstars.2016.2626256","title":"Assimilation of Synthetic Remotely Sensed Soil Moisture in Environment Canada's MESH Model","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of Waterloo","funders":"","keywords":"Data assimilation; Water content; Environmental science; Satellite; Remote sensing; Ensemble Kalman filter; Moisture; Assimilation (phonology); Land cover; Soil science; Computer science; Meteorology; Kalman filter; Geology; Extended Kalman filter; Land use; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002325968,0.0003007763,0.0002210816,0.0002161167,0.0004313502,0.000437955,0.0007535068,0.0003388009,0.0007725397],"category_scores_gemma":[0.000731418,0.0001631231,0.0003198162,0.0005086576,0.0002577018,0.0003170871,0.0002616781,0.0003862499,0.0001079052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00352484,"about_ca_system_score_gemma":0.004082304,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8362108,"about_ca_topic_score_gemma":0.8214548,"domain_scores_codex":[0.9998947,0.00001257293,0.000004295441,0.00002752515,0.00003581987,0.00002496549],"domain_scores_gemma":[0.9997516,0.0000518029,0.00001778084,0.00003369397,0.0001260239,0.00001918401],"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":[0.00007593126,0.00003344047,0.008771147,0.00001709212,0.00002236588,0.00003841361,0.00003719417,0.9796912,0.002808873,0.0009432741,0.0006897258,0.006871325],"study_design_scores_gemma":[0.00002142269,0.00001339823,0.005044885,0.000001571335,0.000004912299,0.000003461703,0.00001987564,0.9930148,0.001181348,0.0001445,0.0005400817,0.000009662744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9759852,0.0000554671,0.01626153,0.0001112784,0.00003033476,0.00004819024,0.002517556,0.0004309283,0.004559511],"genre_scores_gemma":[0.98626,0.00004733395,0.01063412,0.00002101707,0.000002559376,0.00003010439,0.001905901,0.0000287243,0.00107008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8362108,"threshold_uncertainty_score":0.3295076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01368680972942835,"score_gpt":0.1941482102628262,"score_spread":0.1804614005333978,"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."}}