{"id":"W2548513536","doi":"10.1109/igarss.2016.7729433","title":"Empirical model for surface soil moisture estimation over wheat fields using C-band polarimetric SAR","year":2016,"lang":"en","type":"article","venue":"","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"C band; Remote sensing; Water content; L band; Polarimetry; Environmental science; Soil science; Synthetic aperture radar; Moisture; Field (mathematics); Geology; Meteorology; Geography; Mathematics; Physics; Geotechnical engineering; Scattering; Optics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004667949,0.0005315971,0.0004017678,0.0004849242,0.0001307593,0.0005512938,0.0009564288,0.0006123174,0.0009748845],"category_scores_gemma":[0.001218774,0.0003298337,0.0004332216,0.000410479,0.0002627225,0.0008438222,0.0003125416,0.0004582013,0.0005554005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004555102,"about_ca_system_score_gemma":0.0003836584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007034528,"about_ca_topic_score_gemma":0.004899304,"domain_scores_codex":[0.9998023,0.00004948407,0.000009384621,0.000079638,0.00003846542,0.00002066666],"domain_scores_gemma":[0.9997111,0.0001280421,0.00004652725,0.00003877944,0.00006736995,0.000008296003],"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":[0.00003539359,0.00003381714,0.00441503,0.00003659548,0.00004788131,0.00005724011,0.00002721444,0.9743692,0.002940248,0.00131082,0.0004517044,0.0162748],"study_design_scores_gemma":[0.000003661853,0.000006173726,0.001120807,0.000002845283,0.000005476962,0.00001412149,0.000003664365,0.9980296,0.0002195613,0.0004280657,0.000162104,0.00000386125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2271786,0.0005044052,0.7660843,0.0002995043,0.00003977069,0.00006123474,0.0008958391,0.001227433,0.003708942],"genre_scores_gemma":[0.9673799,0.000424638,0.02802517,0.00006383382,0.00003163766,0.0001198763,0.0009649781,0.0001197728,0.00287027],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007034528,"threshold_uncertainty_score":0.01398718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03223816749414737,"score_gpt":0.290742464009379,"score_spread":0.2585042965152317,"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."}}