{"id":"W2909983709","doi":"10.1080/07038992.2018.1527683","title":"Optimal Compact Polarimetric Parameters and Texture Features for Discriminating Sea Ice Types during Winter and Advanced Melt","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Environment and Climate Change Canada; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Marine Environmental Observation Prediction and Response Network","keywords":"Sea ice; Polarimetry; Synthetic aperture radar; Remote sensing; Arctic; Meteorology; Environmental science; Geology; Geography; Oceanography; Scattering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.000295608,0.0004335666,0.0003405834,0.001195166,0.0001525955,0.0005465981,0.0001726323,0.0002132439,0.0003445295],"category_scores_gemma":[0.0005421803,0.0001554413,0.0003706121,0.0006840468,0.0001733938,0.0004591271,0.0001929352,0.0002074888,0.0001539163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002337807,"about_ca_system_score_gemma":0.0003522414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004243452,"about_ca_topic_score_gemma":0.005408314,"domain_scores_codex":[0.9999042,0.0000150654,0.000005569782,0.00002821645,0.0000189591,0.00002795332],"domain_scores_gemma":[0.999844,0.0000482152,0.00003373509,0.00001240763,0.00004066746,0.00002103676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002279037,0.0005263918,0.1263917,0.0002153856,0.0001211543,0.0003262505,0.0003109365,0.2182043,0.2222677,0.0007322031,0.001948386,0.4266766],"study_design_scores_gemma":[0.00003549688,0.0001685324,0.1316249,0.00001583463,0.00007416962,0.00009601229,0.0002360507,0.8441101,0.02249281,0.0004911979,0.0006221115,0.00003273867],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9708355,0.0001452002,0.02776784,0.00002661802,0.0000108133,0.00002345354,0.0003559397,0.0001545923,0.0006800644],"genre_scores_gemma":[0.9872621,0.00007702571,0.01162892,0.000006010069,0.000007530972,0.00001182939,0.000796143,0.00002073297,0.0001896907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004243452,"threshold_uncertainty_score":0.008437514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009935148401383672,"score_gpt":0.2190584786560576,"score_spread":0.2091233302546739,"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."}}