{"id":"W2914338499","doi":"10.1029/2018wr023247","title":"Improving Permafrost Modeling by Assimilating Remotely Sensed Soil Moisture","year":2019,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Guelph","funders":"Bundesministerium für Bildung und Forschung; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; ArcticNet","keywords":"Permafrost; Environmental science; Water content; Soil science; Soil water; Pedotransfer function; Soil morphology; Moisture; Soil thermal properties; Hydrology (agriculture); Soil organic matter; Field capacity; Geology; Hydraulic conductivity; Geotechnical engineering; Meteorology; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0003660331,0.000633161,0.0004188628,0.0003834052,0.0003469053,0.0007742791,0.0009491167,0.0006825946,0.001030326],"category_scores_gemma":[0.0009411254,0.0003733317,0.0006860747,0.0004870315,0.0002434131,0.0008321608,0.0004531062,0.0005545637,0.0001612105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008087734,"about_ca_system_score_gemma":0.001274626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1252587,"about_ca_topic_score_gemma":0.08276884,"domain_scores_codex":[0.9999033,0.0000238226,0.000008180689,0.0000333749,0.00001660697,0.00001464927],"domain_scores_gemma":[0.9997384,0.00008638969,0.00003110124,0.000041812,0.00006850038,0.00003376919],"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":[0.00003318984,0.00005522345,0.01594537,0.000022831,0.00007937719,0.00003628674,0.00002669708,0.9698121,0.0028086,0.0002478129,0.0002839973,0.01064846],"study_design_scores_gemma":[0.000009085868,0.000008496132,0.002529892,0.000002944432,0.000006588999,0.000003405108,0.0000063255,0.9967372,0.0003612686,0.0001168261,0.0002117274,0.000006350342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9536107,0.0003565515,0.03818065,0.0003852836,0.00006456415,0.00004279346,0.001056988,0.001649807,0.004652699],"genre_scores_gemma":[0.9845549,0.00009113227,0.01440786,0.00004700801,0.00001671652,0.00001858188,0.0004582799,0.00007958566,0.0003259706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1252587,"threshold_uncertainty_score":0.2490592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0610026616849913,"score_gpt":0.2852391770552316,"score_spread":0.2242365153702403,"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."}}