{"id":"W2116588317","doi":"10.5194/hess-15-345-2011","title":"Soil surface moisture estimation over a semi-arid region using ENVISAT ASAR radar data for soil evaporation evaluation","year":2011,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":150,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Centre National de la Recherche Scientifique; Agence Universitaire de la Francophonie; European Space Agency; Strong","keywords":"Environmental science; Water content; Arid; Remote sensing; Radar; Vegetation (pathology); Evaporation; Moisture; Soil science; Irrigation; Hydrology (agriculture); Meteorology; Geology; Geography; Geotechnical engineering; Agronomy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001983256,0.0001503114,0.0001844476,0.00004544637,0.0006834757,0.00005146156,0.0002256772,0.0001642241,0.00001764808],"category_scores_gemma":[0.00006776615,0.0001191168,0.00002845451,0.0002129511,0.0004696853,0.0006882573,0.000120732,0.00008147198,0.00001773076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004769544,"about_ca_system_score_gemma":0.00005052073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002202783,"about_ca_topic_score_gemma":0.002097532,"domain_scores_codex":[0.9982709,0.0002214366,0.0002562188,0.0006032298,0.0003766464,0.0002715945],"domain_scores_gemma":[0.9992749,0.00007506958,0.0002217237,0.0003443625,0.00001864845,0.00006530726],"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":[0.000366731,0.0001771803,0.1284199,0.0003043554,0.0001077263,0.00003366732,0.008880317,0.7060249,0.02761055,0.0008403868,0.002063232,0.125171],"study_design_scores_gemma":[0.000316377,0.0001120646,0.02623617,0.000041103,0.00007553475,0.0001186962,0.0003290272,0.9710841,0.0008913132,0.0004745994,0.0001659845,0.0001550736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858466,0.0002707118,0.008471634,0.000112086,0.0004268034,0.0004518852,0.000003927705,0.00004169982,0.004374615],"genre_scores_gemma":[0.9956621,0.00001137006,0.004070852,0.00009982204,0.00007257963,0.000001550956,0.00003620162,0.000007281409,0.00003826998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2650591,"threshold_uncertainty_score":0.5256811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08582641913630515,"score_gpt":0.2839342615715675,"score_spread":0.1981078424352623,"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."}}