{"id":"W4296455993","doi":"10.3390/rs14184624","title":"Global Evaluation of SMAP/Sentinel-1 Soil Moisture Products","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Environmental science; Remote sensing; Water content; Vegetation (pathology); Radar; Synthetic aperture radar; Atmospheric sciences; Meteorology; Geology; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.005193801,0.0009502219,0.0004437442,0.001398908,0.000180543,0.0007838861,0.0007049876,0.0005887417,0.0007027183],"category_scores_gemma":[0.004553623,0.0002219712,0.0004547912,0.001403692,0.0002793063,0.001395078,0.000867821,0.0003655531,0.0003066904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004587104,"about_ca_system_score_gemma":0.0004175348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0070261,"about_ca_topic_score_gemma":0.006738568,"domain_scores_codex":[0.9987168,0.0004342577,0.0000792646,0.0002455452,0.0004219303,0.0001021914],"domain_scores_gemma":[0.9978697,0.0005209646,0.0002921634,0.0003215227,0.0008832632,0.0001123517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001162061,0.0004225801,0.2931972,0.0007972395,0.001165078,0.0004959836,0.0004475714,0.4722495,0.03641677,0.002589259,0.01470665,0.1763501],"study_design_scores_gemma":[0.0001385908,0.0005009866,0.2960827,0.00008970244,0.000202384,0.0001525939,0.0003752277,0.6766669,0.01450296,0.0007710708,0.01042327,0.00009360746],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9473938,0.0004577058,0.03163436,0.0002615887,0.0001131449,0.0001557196,0.01236426,0.002268134,0.005351146],"genre_scores_gemma":[0.9482186,0.0001438438,0.03005321,0.00007851177,0.00003545653,0.00008021021,0.0205309,0.0002118476,0.0006473105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0070261,"threshold_uncertainty_score":0.02746779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01899311144770796,"score_gpt":0.2528493433195759,"score_spread":0.233856231871868,"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."}}