{"id":"W2971467437","doi":"10.1029/2019ms001729","title":"Version 4 of the SMAP Level‐4 Soil Moisture Algorithm and Data Product","year":2019,"lang":"en","type":"article","venue":"Journal of Advances in Modeling Earth Systems","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":279,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Space Agency; Environment and Climate Change Canada","keywords":"Environmental science; Water content; Ensemble Kalman filter; Radiometer; Data assimilation; Moisture; Standard deviation; Soil science; Satellite; Atmospheric sciences; Meteorology; Remote sensing; Kalman filter; Mathematics; Geology; Extended Kalman filter; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009521877,0.0006883607,0.0003999069,0.001222984,0.0002018786,0.0009747415,0.001271084,0.0005007648,0.02034813],"category_scores_gemma":[0.003157434,0.0005977523,0.0004332939,0.001420092,0.0001434098,0.001177773,0.0008144734,0.0007440237,0.01583455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004087157,"about_ca_system_score_gemma":0.0006708148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00571203,"about_ca_topic_score_gemma":0.004450072,"domain_scores_codex":[0.9995409,0.0000707693,0.00005282411,0.00008503711,0.0001993622,0.00005115638],"domain_scores_gemma":[0.9991695,0.000125113,0.0001127796,0.0002030453,0.0003516777,0.00003790441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009939441,0.0002467619,0.03621061,0.0004711808,0.0002356004,0.0001878508,0.0001658013,0.09376116,0.01591679,0.007916014,0.4098005,0.4340939],"study_design_scores_gemma":[0.0007474874,0.000116377,0.0397707,0.00009843286,0.00006243136,0.0001024931,0.00006029682,0.5652956,0.0247271,0.01062232,0.3582585,0.0001382949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08159807,0.0003019164,0.4492037,0.000502717,0.0002711316,0.001041746,0.2550408,0.1793081,0.03273178],"genre_scores_gemma":[0.1975861,0.0001776364,0.3904742,0.0003700199,0.00009804495,0.001854615,0.3840392,0.01158357,0.01381655],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02034813,"threshold_uncertainty_score":0.06807131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01885836765910421,"score_gpt":0.2507583644911058,"score_spread":0.2318999968320016,"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."}}