{"id":"W2985894533","doi":"10.1109/igarss.2019.8898418","title":"Soil Moisture Estimation From Smap Observations Using Long Short- Term Memory (LSTM)","year":2019,"lang":"en","type":"article","venue":"","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Long short term memory; Water content; Moderate-resolution imaging spectroradiometer; Environmental science; Artificial neural network; Term (time); Brightness temperature; Vegetation (pathology); Moisture; Estimation; Computer science; Soil science; Remote sensing; Recurrent neural network; Artificial intelligence; Meteorology; Geology; Engineering","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":[0.0002257106,0.0005185634,0.0003175503,0.0005832546,0.0001505934,0.0003657889,0.0005006276,0.0004941332,0.001138015],"category_scores_gemma":[0.0008838591,0.000215271,0.0003332489,0.0009633073,0.0001240283,0.001051011,0.0003296753,0.0006106072,0.0004660058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002233316,"about_ca_system_score_gemma":0.0002553171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003884209,"about_ca_topic_score_gemma":0.007810038,"domain_scores_codex":[0.9999162,0.00001197023,0.000007060274,0.00003188644,0.00002126454,0.00001166574],"domain_scores_gemma":[0.9998522,0.00004472655,0.00003072425,0.00001514608,0.00005161484,0.0000056218],"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":[0.0001811787,0.0001678051,0.01671658,0.0005130187,0.0002625213,0.0002389787,0.0001388246,0.2120679,0.07491182,0.001334719,0.005557709,0.6879089],"study_design_scores_gemma":[0.0000150941,0.00003791001,0.01382236,0.00003088261,0.00003557802,0.00006167278,0.0000331778,0.967611,0.01462719,0.001719614,0.001978119,0.00002734154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2295272,0.00156874,0.7584853,0.000349661,0.0002456559,0.00006909228,0.002713983,0.003359969,0.003680405],"genre_scores_gemma":[0.8536172,0.0008847406,0.140742,0.0001614356,0.0001144035,0.00009551345,0.002418132,0.0001414145,0.001825242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003884209,"threshold_uncertainty_score":0.007723153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02458732886630293,"score_gpt":0.2386951081321293,"score_spread":0.2141077792658264,"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."}}