{"id":"W2408718570","doi":"10.1109/jstars.2016.2560342","title":"Dual-Polarimetric C- and X-Band SAR Data for Coastline Extraction","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Space Agency; Universitat de Barcelona; European Space Agency; Nova Southeastern University","keywords":"Synthetic aperture radar; Remote sensing; Ground truth; Polarimetry; Computer science; Radar imaging; Pixel; Inverse synthetic aperture radar; L band; C band; Side looking airborne radar; Radar; Geology; Artificial intelligence; Bistatic radar; Scattering; Physics; Telecommunications; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003071936,0.0001118879,0.0001959845,0.000216194,0.00008431448,0.00003497381,0.00006606785,0.0001092941,0.000001248874],"category_scores_gemma":[0.0001115354,0.00008486795,0.0000165763,0.0003636764,0.00003721242,0.0001233091,0.00001163604,0.0001649342,1.678096e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003308249,"about_ca_system_score_gemma":0.00004210745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002108064,"about_ca_topic_score_gemma":0.00005020819,"domain_scores_codex":[0.9992197,0.00001163896,0.0003711569,0.0001381529,0.0001181778,0.000141189],"domain_scores_gemma":[0.9992094,0.0002505553,0.0001141607,0.0001993756,0.0001753472,0.00005115976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001151713,0.000009942204,0.00008648602,0.00002466917,0.0000268761,0.000001696597,0.00002874344,0.00001171957,0.07316428,0.0002110608,0.0002349238,0.9261881],"study_design_scores_gemma":[0.001750553,0.0001057971,0.02195838,0.0003509798,0.0001239496,0.0004393345,0.00006839647,0.08163237,0.09058528,0.006182394,0.7963563,0.0004462927],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1674901,0.0004677356,0.8309045,0.0006406325,0.000117178,0.0001978665,0.00002154,0.00004500019,0.0001154686],"genre_scores_gemma":[0.2940354,0.001500099,0.7040817,0.00003312958,0.0002909034,6.743879e-8,0.000007795967,0.00002027389,0.00003059477],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9257418,"threshold_uncertainty_score":0.3460814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03505523693027194,"score_gpt":0.2598534618399853,"score_spread":0.2247982249097133,"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."}}