{"id":"W2912230086","doi":"10.1109/tgrs.2019.2893908","title":"Fine-Scale SAR Soil Moisture Estimation in the Subarctic Tundra","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Tundra; Permafrost; Remote sensing; Environmental science; Backscatter (email); Subarctic climate; Water content; Soil science; Synthetic aperture radar; Scale (ratio); Computer science; Geology; Physics; Arctic","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003755049,0.0001698907,0.0001481795,0.00008693634,0.0003839838,0.00009123653,0.0001383503,0.00009896406,0.0000177109],"category_scores_gemma":[0.00000754168,0.0001162698,0.00006082989,0.000603996,0.0002958497,0.0002491009,0.000003961802,0.0003533593,0.0001505611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008296272,"about_ca_system_score_gemma":0.00001656639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004557907,"about_ca_topic_score_gemma":0.01044129,"domain_scores_codex":[0.9985704,0.00008761405,0.0001927604,0.0004191657,0.0004013888,0.0003287086],"domain_scores_gemma":[0.99944,0.0001225139,0.00005341212,0.0003133378,0.000009045814,0.00006164774],"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":[0.00001808027,0.00003504026,0.0001644443,0.0000119788,0.000003079556,0.00001684957,0.002585752,0.02073356,0.008280738,0.000001481287,0.00003251201,0.9681165],"study_design_scores_gemma":[0.000622684,0.0001831738,0.08707988,0.0001885043,0.00004342198,0.0004931107,0.001753189,0.8980564,0.009298491,0.0009234866,0.0008688277,0.0004888113],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.839136,0.00001649829,0.1532881,0.001228661,0.0005284493,0.0002418785,6.524342e-7,0.00003526261,0.005524479],"genre_scores_gemma":[0.9869276,0.00002239422,0.01175427,0.0006395034,0.00002319238,3.353651e-8,6.611542e-7,0.00001083557,0.0006214572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9676276,"threshold_uncertainty_score":0.6890222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007010134572573414,"score_gpt":0.2156104387317081,"score_spread":0.2086003041591347,"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."}}