{"id":"W4200353284","doi":"10.22541/au.163777510.05550930/v1","title":"High-resolution snow depth prediction using Random Forest algorithm with topographic parameters and an ecosystem map: a case study in the Greiner Watershed, Nunavut","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Snow; Permafrost; Arctic; Watershed; Environmental science; Physical geography; Vegetation (pathology); Ecosystem; Hydrology (agriculture); Geology; Ecology; Geography; Geomorphology; Oceanography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006001356,0.0005930054,0.0003683329,0.0005997905,0.0005941709,0.0005614464,0.0007978969,0.000520898,0.0004361949],"category_scores_gemma":[0.001105314,0.0002624479,0.0003727374,0.0009416788,0.0003054491,0.000315002,0.0002507891,0.0002471228,0.00009103813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002460121,"about_ca_system_score_gemma":0.00200325,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6966761,"about_ca_topic_score_gemma":0.7255191,"domain_scores_codex":[0.9998206,0.00004820281,0.000009493454,0.00004997884,0.00003248354,0.00003914084],"domain_scores_gemma":[0.9994627,0.0002470654,0.00002573783,0.00003594936,0.0001793138,0.00004932669],"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":[0.0004074563,0.0004356631,0.1243141,0.00007283043,0.00009429416,0.00175602,0.0003574424,0.815254,0.004076757,0.0003999215,0.0009178545,0.05191373],"study_design_scores_gemma":[0.00002118478,0.00003137798,0.02636003,0.000005006659,0.00001602041,0.00005488656,0.0001385687,0.9723938,0.0006980756,0.00008763565,0.0001839849,0.000009488147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949411,0.00008605948,0.003939957,0.00005786908,0.000005148841,0.00003322426,0.0002420243,0.0001373895,0.0005573477],"genre_scores_gemma":[0.9868053,0.00004268942,0.01229344,0.000006804852,0.000003467848,0.00001335656,0.0003617242,0.00001521148,0.0004579218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3033239,"threshold_uncertainty_score":0.6102205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04823524701132127,"score_gpt":0.2534447148685632,"score_spread":0.2052094678572419,"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."}}