{"id":"W2913379186","doi":"10.1109/lgrs.2019.2892896","title":"Icebergs in Sea Ice With TanDEM-X Interferometry","year":2019,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Geological Survey; Canadian Space Agency; Deutsches Zentrum für Luft- und Raumfahrt","keywords":"Interferometric synthetic aperture radar; Iceberg; Remote sensing; Synthetic aperture radar; Sea ice; Geology; Digital elevation model; Interferometry; Geodesy; Sea ice concentration; Arctic; Elevation (ballistics); Arctic ice pack; Sea ice thickness; Oceanography; Optics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0003049047,0.0003281713,0.0001542348,0.00102707,0.0001236611,0.0003354995,0.0002188557,0.0001878017,0.0004398635],"category_scores_gemma":[0.0004234062,0.0001231771,0.0002360969,0.0006508231,0.0001526452,0.0006099231,0.0003190317,0.0001400338,0.0002136295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001240628,"about_ca_system_score_gemma":0.000188638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001401867,"about_ca_topic_score_gemma":0.002288994,"domain_scores_codex":[0.9998499,0.00002504179,0.000008048892,0.00004193727,0.00006154716,0.00001350043],"domain_scores_gemma":[0.9998116,0.00003735915,0.00006931524,0.00002530232,0.00004634498,0.00001009264],"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":[0.000581292,0.0002091755,0.1805888,0.0003001618,0.0002257066,0.0004709606,0.0003959949,0.07107952,0.2534331,0.002362007,0.002313586,0.4880397],"study_design_scores_gemma":[0.00005476444,0.0005845654,0.3077202,0.00005446257,0.0001591749,0.001421003,0.0003283317,0.585325,0.09293743,0.003466292,0.007875852,0.00007297655],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7605091,0.001185427,0.2311347,0.00008289801,0.00005170333,0.00006888228,0.0006486786,0.001123684,0.005195057],"genre_scores_gemma":[0.8816478,0.0004538469,0.1162136,0.00004442784,0.00005102331,0.00002558469,0.0005596045,0.00002607542,0.0009781363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001401867,"threshold_uncertainty_score":0.002787352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01087510378756218,"score_gpt":0.1969482165431743,"score_spread":0.1860731127556121,"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."}}