{"id":"W3172423879","doi":"10.5194/egusphere-egu21-14317","title":"Calibration of sea ice drift forecasts using random forest algorithms","year":2021,"lang":"en","type":"article","venue":"","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Buoy; Sea ice; Calibration; Initialization; Meteorology; Environmental science; Arctic; Algorithm; Random forest; Data set; Climatology; Arctic ice pack; Computer science; Oceanography; Geology; Machine learning; Mathematics; Statistics; Geography; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002605461,0.0009353096,0.0007070731,0.001341085,0.0003875718,0.0006198003,0.0007764088,0.0006363519,0.0007311177],"category_scores_gemma":[0.006204023,0.0003208824,0.0005900034,0.0008649086,0.0002831382,0.0006602583,0.0004658743,0.0007290995,0.0004253151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005681753,"about_ca_system_score_gemma":0.0009929489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01057339,"about_ca_topic_score_gemma":0.00662636,"domain_scores_codex":[0.9990094,0.0003794524,0.00006110613,0.0002045694,0.0002406835,0.0001047512],"domain_scores_gemma":[0.9971403,0.001344278,0.0003101904,0.000209675,0.0009336949,0.00006188001],"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":[0.0001105802,0.00004657007,0.001912333,0.00002699251,0.00005127517,0.00002786411,0.00001682562,0.9000293,0.001663093,0.0002755455,0.0004775907,0.09536206],"study_design_scores_gemma":[0.000006400223,0.00001108946,0.0004658076,0.0000035709,0.000004010068,0.000005407594,0.000003034589,0.9982376,0.0009722463,0.0001868571,0.00009851239,0.000005464725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2018518,0.0005253207,0.7922306,0.000106406,0.00009825524,0.0001323804,0.0002531702,0.002485913,0.002316153],"genre_scores_gemma":[0.7764207,0.0001895336,0.2216419,0.00005010056,0.00003246213,0.0001089452,0.0006693483,0.0001035175,0.0007835073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01057339,"threshold_uncertainty_score":0.02102369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01887017327548605,"score_gpt":0.2242433300664159,"score_spread":0.2053731567909299,"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."}}