{"id":"W2883945992","doi":"10.1109/lgrs.2018.2852143","title":"Sea Ice Sensing From GNSS-R Data Using Convolutional Neural Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":127,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pixel; Convolutional neural network; Remote sensing; Computer science; Radiometer; Satellite; Microwave imaging; Artificial intelligence; Image resolution; Artificial neural network; Computer vision; Microwave; Geology; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002513264,0.0004567532,0.0001848739,0.0004403871,0.0001725868,0.0003016646,0.0003869925,0.0003081127,0.0005262691],"category_scores_gemma":[0.0005760056,0.0001577914,0.0002306448,0.0004273816,0.0001496611,0.0004982491,0.0003185017,0.0002425957,0.0002313423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003596459,"about_ca_system_score_gemma":0.0003249361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007127749,"about_ca_topic_score_gemma":0.01394474,"domain_scores_codex":[0.9999013,0.00001032868,0.000005827075,0.00002955578,0.00003228636,0.0000206491],"domain_scores_gemma":[0.99988,0.00002657731,0.00001976409,0.00002387499,0.00004220341,0.000007645809],"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":[0.000357243,0.0001187972,0.01970585,0.0001603434,0.0001526379,0.0002739559,0.00009682039,0.1973964,0.1478336,0.001952764,0.001984969,0.6299666],"study_design_scores_gemma":[0.000007071433,0.0000519358,0.006275159,0.00001098821,0.00003216192,0.00004937077,0.00002026907,0.9583107,0.03331009,0.000488065,0.00143166,0.00001250931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4902209,0.0008971597,0.5004507,0.0002207692,0.0001840152,0.00007188128,0.0004866744,0.002052381,0.00541546],"genre_scores_gemma":[0.8969277,0.0002678732,0.1002373,0.00007160435,0.00003950143,0.00002625827,0.0005437254,0.00002673361,0.001859336],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007127749,"threshold_uncertainty_score":0.01417255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02916829971646263,"score_gpt":0.2507385508453024,"score_spread":0.2215702511288398,"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."}}