{"id":"W4210826704","doi":"10.3390/rs14030776","title":"Enhancing Spatial Resolution of SMAP Soil Moisture Products through Spatial Downscaling over a Large Watershed: A Case Study for the Susquehanna River Basin in the Northeastern United States","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Downscaling; Environmental science; Watershed; Brightness temperature; Remote sensing; Image resolution; Climatology; Brightness; Meteorology; Geology; Precipitation; Geography","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":[0.001760371,0.0002696622,0.0002901827,0.00009099916,0.001041451,0.00005629307,0.0002223386,0.00006619854,0.000008520353],"category_scores_gemma":[0.0001375749,0.0001732361,0.0001139629,0.0007074723,0.0001736277,0.0001189782,0.0003490411,0.0004922511,0.000002492797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003436707,"about_ca_system_score_gemma":0.00003264382,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3851547,"about_ca_topic_score_gemma":0.2755152,"domain_scores_codex":[0.9969417,0.0007887789,0.0004856397,0.0005416307,0.0006877522,0.0005544287],"domain_scores_gemma":[0.998742,0.0003686801,0.000245701,0.0005598302,0.00004802593,0.00003573338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001429097,0.001052827,0.01225789,0.000181661,0.0002690644,0.004171147,0.4899767,0.2727394,0.02663586,0.000008548535,0.0003960246,0.1908818],"study_design_scores_gemma":[0.002588248,0.0004766155,0.02098745,0.0001040303,0.0002527008,0.001585564,0.09604572,0.8684089,0.004925222,0.0002039143,0.003880403,0.0005412918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9788097,0.00006146538,0.01818207,0.0009470298,0.0003629386,0.00144918,0.00001396255,0.00003223871,0.000141485],"genre_scores_gemma":[0.9978704,0.00000607244,0.001212347,0.0005559109,0.0002275221,3.571958e-7,0.00004941629,0.00003878093,0.00003912861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5956694,"threshold_uncertainty_score":0.8010108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01497010856776391,"score_gpt":0.2443802022332288,"score_spread":0.2294100936654649,"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."}}