{"id":"W4393282243","doi":"10.1016/j.asr.2024.03.061","title":"Retrieval of sea ice thickness from FY-3E data using Random Forest method","year":2024,"lang":"en","type":"article","venue":"Advances in Space Research","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"National Natural Science Foundation of China","keywords":"Global Positioning System; Sea ice; Remote sensing; Environmental science; Meteorology; Arctic; Satellite; Consistency (knowledge bases); Inversion (geology); Geodesy; Geology; Climatology; Computer science; Oceanography; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004310646,0.0001367509,0.0002937515,0.0002325391,0.0001556275,0.0000908742,0.0008524358,0.0001026009,0.0005505192],"category_scores_gemma":[0.0008974267,0.000109157,0.00004359707,0.001192184,0.0003681607,0.001147711,0.0001920864,0.0007362918,0.00005745576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000224535,"about_ca_system_score_gemma":0.0003222367,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01509919,"about_ca_topic_score_gemma":0.01083414,"domain_scores_codex":[0.9970964,0.0005650137,0.0002921588,0.0005664051,0.0009335095,0.0005465263],"domain_scores_gemma":[0.9942415,0.004869085,0.0000487572,0.0006210395,0.0001087247,0.0001109101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001628197,0.00005991525,0.7364781,0.0007126736,0.00009419657,0.0004716332,0.001723347,0.05291856,0.0003436109,0.001614302,0.0002706184,0.2036848],"study_design_scores_gemma":[0.0005822305,0.00007863509,0.01060488,0.0004069532,0.00002303598,0.00002875261,0.002074471,0.9551258,0.0001096316,0.01572654,0.01505011,0.0001889618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6974799,0.05750471,0.2202191,0.002490728,0.002057766,0.001078419,0.002237076,0.000116972,0.01681532],"genre_scores_gemma":[0.9407055,0.00557105,0.05291268,0.00002184512,0.0002445274,6.178343e-7,0.0003232543,0.00001057431,0.0002099998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9022073,"threshold_uncertainty_score":0.9914594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08758199784130452,"score_gpt":0.4163169047448678,"score_spread":0.3287349069035633,"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."}}