{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001104124,0.0001592496,0.0001922857,0.00003610013,0.0003226195,0.00001694235,0.0001275379,0.00005096344,0.00007609049],"category_scores_gemma":[0.0001765195,0.0001569812,0.00008166677,0.000747194,0.0001177354,0.0000702753,0.0003098269,0.0001769485,0.00002951219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005949254,"about_ca_system_score_gemma":0.00006185388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002158397,"about_ca_topic_score_gemma":0.000667889,"domain_scores_codex":[0.997372,0.0003363963,0.0002739053,0.0004256043,0.00130024,0.0002918356],"domain_scores_gemma":[0.9992566,0.00002263013,0.0001915017,0.0004063591,0.00006504066,0.00005790194],"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":[0.00003772191,0.00005169224,0.003217871,0.0000198476,0.00003515428,0.00003153362,0.0006937765,0.08989787,0.06212957,0.000008339793,0.004371779,0.8395048],"study_design_scores_gemma":[0.001516294,0.00009317182,0.2316132,0.00006613306,0.0003766442,0.0008210471,0.00110163,0.7317749,0.01502771,0.004315574,0.0125545,0.0007392184],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.917359,0.0001764627,0.0003168732,0.0006676943,0.0005520287,0.0002546281,0.000001511904,0.00005366841,0.08061814],"genre_scores_gemma":[0.9963332,0.000003754102,0.00302836,0.0002309368,0.0001458427,1.081259e-8,0.0000138224,0.00001764685,0.0002263832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8387656,"threshold_uncertainty_score":0.6401507,"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."}}