{"id":"W2789796477","doi":"10.1109/lgrs.2018.2805259","title":"Correction of Forcing-Related Spatial Artifacts in a Land Surface Model by Satellite Soil Moisture Data Assimilation","year":2018,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Marshall Space Flight Center; National Aeronautics and Space Administration","keywords":"Data assimilation; Environmental science; Remote sensing; Forcing (mathematics); Satellite; Radiometer; Water content; Meteorology; Atmospheric sciences; Geology; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006096003,0.0003861392,0.0002969025,0.0002216141,0.0003227567,0.0005323518,0.0005616961,0.0004479422,0.000512129],"category_scores_gemma":[0.002501102,0.0002574768,0.0003649046,0.0005018182,0.0003081235,0.000419108,0.0004403407,0.000510291,0.0001182904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008282733,"about_ca_system_score_gemma":0.001330515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05869513,"about_ca_topic_score_gemma":0.05743781,"domain_scores_codex":[0.9997742,0.00004455091,0.00002272315,0.000069646,0.00006216233,0.00002663712],"domain_scores_gemma":[0.9995469,0.0001252771,0.00006244091,0.0001272244,0.0001164808,0.00002159945],"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":[0.0001805744,0.0001226364,0.0406427,0.00004406162,0.0001256646,0.0001163971,0.00007143631,0.9035361,0.02615026,0.001581172,0.00117852,0.02625046],"study_design_scores_gemma":[0.00003376675,0.0000203951,0.01010394,0.000002479635,0.00001714435,0.00001081733,0.00001007213,0.9845988,0.004591757,0.0002146352,0.0003848258,0.00001141291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9222273,0.0001276589,0.0735385,0.0002659878,0.0001058279,0.00005630766,0.0006873292,0.001369245,0.001621768],"genre_scores_gemma":[0.9827703,0.00004405803,0.01614291,0.00004232658,0.00001772445,0.00002637704,0.0003557159,0.0001192326,0.0004813687],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.05869513,"threshold_uncertainty_score":0.116707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01370392955592053,"score_gpt":0.2302982761167024,"score_spread":0.2165943465607818,"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."}}