{"id":"W4401345257","doi":"10.1080/17538947.2024.2385079","title":"Evaluating soil moisture retrieval in Arctic and sub-Arctic environments using passive microwave satellite data","year":2024,"lang":"en","type":"article","venue":"International Journal of Digital Earth","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Université de Sherbrooke; Carleton University; Université du Québec à Trois-Rivières; Center for Northern Studies","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Centre National d’Etudes Spatiales; Canadian Space Agency; Agence Nationale de la Recherche","keywords":"Satellite; Remote sensing; Arctic; Microwave; Environmental science; The arctic; Geography; Computer science; Geology; Engineering; Oceanography; Telecommunications","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":[0.0007815359,0.0006475553,0.0004201709,0.0007402587,0.0002310311,0.0004865041,0.0003360449,0.0004253612,0.0003256124],"category_scores_gemma":[0.001034112,0.0001694274,0.0003997931,0.000823859,0.0001529702,0.0005929605,0.0002919537,0.0001676054,0.0001672461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002888269,"about_ca_system_score_gemma":0.0003501789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01972273,"about_ca_topic_score_gemma":0.0182492,"domain_scores_codex":[0.9998001,0.00004773034,0.00001362764,0.00004881966,0.00005161653,0.00003811657],"domain_scores_gemma":[0.9996793,0.0001233243,0.00003241966,0.00003933245,0.00009302665,0.00003259068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001282754,0.0008081105,0.2469021,0.0003536328,0.0005834776,0.0005995459,0.0002463757,0.4269077,0.1746985,0.0003897786,0.001198081,0.14603],"study_design_scores_gemma":[0.0001291915,0.000321426,0.1977091,0.00001900213,0.0001702004,0.0000847094,0.0002291835,0.7598634,0.04019685,0.0001588203,0.001074206,0.00004396632],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954625,0.00008831098,0.003333076,0.00001753033,0.000007107896,0.00001488873,0.00045581,0.0002208928,0.0003998796],"genre_scores_gemma":[0.9884697,0.0000862345,0.009733596,0.00001123251,0.00001181,0.00001140621,0.001465323,0.00003300976,0.0001777866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01972273,"threshold_uncertainty_score":0.03921586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03774684651288945,"score_gpt":0.3062813093280353,"score_spread":0.2685344628151458,"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."}}