{"id":"W3125622824","doi":"10.1109/tgrs.2020.3049031","title":"Automatic Sea-Ice Classification of SAR Images Based on Spatial and Temporal Features Learning","year":2021,"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":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Sea ice; Synthetic aperture radar; Computer science; Artificial intelligence; Sea ice concentration; Remote sensing; Convolutional neural network; Geology; Arctic ice pack; Pattern recognition (psychology); Sea ice thickness; Climatology","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":[0.0003104962,0.0005070859,0.0003188207,0.001055473,0.0001439846,0.0003021654,0.0003413323,0.0002261043,0.0004679838],"category_scores_gemma":[0.0005207426,0.0001683025,0.0004418578,0.0006362303,0.0002066423,0.0005729382,0.0002530695,0.0002807801,0.0002651838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002725763,"about_ca_system_score_gemma":0.0002856739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003788033,"about_ca_topic_score_gemma":0.005723901,"domain_scores_codex":[0.999863,0.00001603605,0.00000968189,0.00004468299,0.00003766337,0.00002886522],"domain_scores_gemma":[0.9997876,0.00003734907,0.00004641106,0.00002473694,0.0000891272,0.00001473674],"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":[0.0003549451,0.000249832,0.0189822,0.0001160529,0.0001263764,0.0002039114,0.0001035412,0.1178478,0.1366079,0.001189149,0.003218417,0.721],"study_design_scores_gemma":[0.000007670477,0.00003974421,0.01133683,0.000006538839,0.00002514823,0.00005235086,0.00003131001,0.9616832,0.02560737,0.0005445225,0.0006543799,0.00001087117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4940761,0.0004254889,0.4992946,0.0001143064,0.00008212653,0.00009199727,0.0005325788,0.002417165,0.002965513],"genre_scores_gemma":[0.8818737,0.0002094143,0.115106,0.00005283272,0.00003845682,0.00005520824,0.0009764033,0.00007086978,0.001617157],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003788033,"threshold_uncertainty_score":0.007531941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0108816965299104,"score_gpt":0.2203861178473078,"score_spread":0.2095044213173974,"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."}}