{"id":"W2922494752","doi":"10.1109/tgrs.2019.2898872","title":"Arctic Sea Ice Classification Using Microwave Scatterometer and Radiometer Data During 2002–2017","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China; National Aeronautics and Space Administration; European Space Agency; Brigham Young University; Japan Aerospace Exploration Agency","keywords":"Special sensor microwave/imager; Scatterometer; Radiometer; Sea ice; Arctic; Remote sensing; Microwave radiometer; Environmental science; Brightness temperature; Synthetic aperture radar; Microwave; Climatology; Sea ice concentration; Archipelago; Data set; Meteorology; Arctic ice pack; Geology; Sea ice thickness; Wind speed; Geography; Oceanography; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000302204,0.0001705389,0.0001708865,0.0002138699,0.0005957429,0.0001582455,0.0001730775,0.00007825196,0.00006534088],"category_scores_gemma":[0.000007362783,0.0001458443,0.00003246875,0.0003097208,0.0002867715,0.0006689527,0.000004514855,0.0002352758,0.00005514157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001488853,"about_ca_system_score_gemma":0.00004062996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002203681,"about_ca_topic_score_gemma":0.0003785476,"domain_scores_codex":[0.9985569,0.00005917804,0.000198554,0.0005927581,0.0002369035,0.000355699],"domain_scores_gemma":[0.999181,0.0001175023,0.00008435838,0.0004446795,0.00003583149,0.0001366655],"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":[0.00009705756,0.00002378819,0.004304612,0.0001857358,0.0000469966,0.00002989816,0.0008425662,0.001014423,0.03208361,0.000001051597,0.000005705088,0.9613646],"study_design_scores_gemma":[0.0003248472,0.00005832133,0.06567849,0.0001477003,0.00005594669,0.0007597354,0.0004691178,0.9315531,0.0005818293,0.00004120344,0.00006722126,0.0002624342],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7840725,0.0000379343,0.2148531,0.0002147211,0.0004742424,0.0001384179,0.00003800513,0.00002164384,0.0001494824],"genre_scores_gemma":[0.9585302,0.000218737,0.04060358,0.0002159886,0.00004175565,1.13809e-8,0.00001099823,0.000006202845,0.0003725383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9611021,"threshold_uncertainty_score":0.5947356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03728347992962533,"score_gpt":0.2424182917678218,"score_spread":0.2051348118381964,"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."}}