{"id":"W4287218469","doi":"10.5194/egusphere-2022-630","title":"Investigating the thermal state of permafrost with Bayesian inverse modeling of heat transfer","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Argonne National Laboratory; Natural Resources Canada; Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research","keywords":"Permafrost; Borehole; Latent heat; Active layer; Environmental science; Climate change; Heat transfer; Atmospheric sciences; Arctic; Climatology; Thermal; Geology; Meteorology; Geography; Geotechnical engineering; Thermodynamics; Materials science; Oceanography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006245935,0.0003110683,0.0003620483,0.000319256,0.0003183232,0.0004988028,0.0006353756,0.0005674979,0.0004703848],"category_scores_gemma":[0.00172682,0.0004619701,0.0004561161,0.0002925837,0.0005051295,0.0005183299,0.0003931057,0.0006131901,0.00007744884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007179555,"about_ca_system_score_gemma":0.001108925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03154681,"about_ca_topic_score_gemma":0.03028061,"domain_scores_codex":[0.9998677,0.00004763784,0.000005606157,0.00003260633,0.00003105833,0.00001530687],"domain_scores_gemma":[0.9996612,0.000199701,0.00006269001,0.00001711386,0.00004267987,0.00001667408],"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":[0.00001949955,0.00002306911,0.004539243,0.0000100515,0.0000223016,0.00001308651,0.00002479301,0.9886696,0.001550963,0.0009847647,0.00006044329,0.004082181],"study_design_scores_gemma":[0.000001622759,0.000001839753,0.0005649258,6.305108e-7,0.000001030765,0.000001127032,0.000002435759,0.9990219,0.0001078096,0.0002706893,0.00002423919,0.000001806905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6062596,0.0001341193,0.3910365,0.0002748139,0.00001479501,0.00003044478,0.0002112975,0.0002674196,0.001770852],"genre_scores_gemma":[0.9738623,0.00004597682,0.02528613,0.00002598493,0.000006043115,0.00002899544,0.0001257675,0.00002216295,0.0005965449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03154681,"threshold_uncertainty_score":0.06272638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05034744963839786,"score_gpt":0.2371131228267374,"score_spread":0.1867656731883395,"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."}}