{"id":"W4221009260","doi":"10.1002/hyp.14546","title":"High‐resolution snow depth prediction using Random Forest algorithm with topographic parameters: A case study in the Greiner watershed, Nunavut","year":2022,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; ArcticDx (Canada); Traffic Injury Research Foundation; Center for Northern Studies; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada; Polar Knowledge Canada","keywords":"Permafrost; Arctic; Snow; Watershed; Environmental science; Physical geography; Hydrology (agriculture); Geology; Remote sensing; Geomorphology; Geography; Oceanography","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":[0.00052943,0.0005310271,0.000299431,0.0004356451,0.0006022477,0.0004958731,0.0007127699,0.0003821893,0.0004187554],"category_scores_gemma":[0.001011978,0.0001854321,0.0002765505,0.0005260026,0.0003358959,0.0001988488,0.0001911438,0.0002519181,0.00008491301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003133193,"about_ca_system_score_gemma":0.002191715,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7715836,"about_ca_topic_score_gemma":0.7758511,"domain_scores_codex":[0.9998804,0.0000330144,0.00000632645,0.00003500583,0.0000193256,0.0000258653],"domain_scores_gemma":[0.9994774,0.0002579258,0.00003005105,0.00002963052,0.0001608542,0.00004417475],"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":[0.0003386277,0.0004167086,0.1519139,0.00004332624,0.00006600274,0.001184759,0.000233506,0.8043614,0.003637634,0.0002954762,0.0008621874,0.03664642],"study_design_scores_gemma":[0.0000190002,0.00003254765,0.02432869,0.000004210679,0.00001001107,0.00003022719,0.0001174548,0.9744888,0.0007444355,0.00007526358,0.0001409822,0.000008458807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967949,0.00005091469,0.002453908,0.00006514654,0.000004062601,0.00002421925,0.0001444337,0.00008396983,0.0003784144],"genre_scores_gemma":[0.9946221,0.00002113512,0.004825141,0.000006135503,0.000002076375,0.00001053394,0.0001857096,0.00000567141,0.0003214116],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2284164,"threshold_uncertainty_score":0.4595233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05702468473649085,"score_gpt":0.2482584956441684,"score_spread":0.1912338109076775,"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."}}