{"id":"W2985810310","doi":"10.1109/igarss.2019.8899233","title":"A Method for Assessing SMAP Core Validation Site Scaling Bias Using Enhanced Sampling and Random Forests","year":2019,"lang":"en","type":"article","venue":"","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Agriculture and Agri-Food Canada","funders":"","keywords":"Environmental science; Remote sensing; Radiometer; Footprint; Sampling (signal processing); Weighting; Water content; Scale (ratio); Soil science; Computer science; Meteorology; Geology; Geography; Cartography","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":[0.01215856,0.0008814794,0.0007002265,0.002007894,0.0007263031,0.0006877584,0.001258016,0.0007501038,0.0008845991],"category_scores_gemma":[0.02974257,0.00042538,0.000958537,0.001618012,0.0005699875,0.0007908239,0.0009762876,0.0008349824,0.0003867707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004319141,"about_ca_system_score_gemma":0.001027667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003600599,"about_ca_topic_score_gemma":0.006197388,"domain_scores_codex":[0.9938725,0.002776867,0.000443957,0.001227051,0.001495453,0.0001842485],"domain_scores_gemma":[0.9856848,0.007279414,0.001395946,0.002803912,0.002710197,0.0001258309],"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":[0.0004443301,0.0002788816,0.06763397,0.0004357239,0.001060832,0.0003320343,0.0005292263,0.1710679,0.03293526,0.01688823,0.004945101,0.7034485],"study_design_scores_gemma":[0.0001522141,0.0002561757,0.03056601,0.0001454993,0.0002546501,0.0007165212,0.0001020569,0.9125442,0.02566478,0.02034809,0.00909472,0.000155134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01322211,0.00005558734,0.9855841,0.00001128274,0.00001778198,0.000153393,0.0001428197,0.0005624454,0.0002504516],"genre_scores_gemma":[0.1305459,0.00004063618,0.8677133,0.00003903901,0.00002632393,0.0006326745,0.0005325173,0.0001588901,0.000310707],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01215856,"threshold_uncertainty_score":0.06430137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07295134396308771,"score_gpt":0.3495873138147219,"score_spread":0.2766359698516341,"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."}}