{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002385149,0.0004848407,0.0004328841,0.001106911,0.0002999627,0.0003078226,0.0004182019,0.0003162725,0.0007739795],"category_scores_gemma":[0.0004698036,0.000197324,0.0005320327,0.0009367633,0.0001222849,0.0005594422,0.0002281712,0.0002737392,0.0004973794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001520671,"about_ca_system_score_gemma":0.000697216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01339715,"about_ca_topic_score_gemma":0.01402952,"domain_scores_codex":[0.9999012,0.00001324153,0.000005226675,0.0000254414,0.0000306785,0.00002428045],"domain_scores_gemma":[0.9998736,0.00002692553,0.00001481009,0.00001635472,0.00005776441,0.00001047826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000683459,0.000277128,0.03396638,0.0002858361,0.0002668458,0.0003428369,0.00007690299,0.3436683,0.1701553,0.001893646,0.005436184,0.4429472],"study_design_scores_gemma":[0.00003385864,0.00002188949,0.01069403,0.00001234547,0.00004156606,0.00006496227,0.0000157166,0.9761084,0.0115607,0.0003688202,0.001050944,0.00002677402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5208875,0.001176204,0.4680509,0.0001243467,0.000165209,0.0000802267,0.003163199,0.002914828,0.00343758],"genre_scores_gemma":[0.7971663,0.0004514122,0.1953757,0.00003628488,0.00006848043,0.00006753773,0.005302827,0.0001436953,0.001387731],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01339715,"threshold_uncertainty_score":0.02663833,"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."}}