{"id":"W2562508592","doi":"10.1109/lgrs.2016.2633572","title":"An Enhanced Probabilistic Posterior Sampling Approach for Synthesizing SAR Imagery With Sea Ice and Oil Spills","year":2016,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs; ArcticNet","keywords":"Synthetic aperture radar; Computer science; Probabilistic logic; Sampling (signal processing); Consistency (knowledge bases); Remote sensing; Segmentation; Sea ice; Oil spill; Artificial intelligence; Environmental science; Computer vision; Geology; Meteorology; Geography","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":[0.001520533,0.0006532923,0.0005701967,0.0009701982,0.0002493992,0.0006096271,0.0006924935,0.000455069,0.0009029236],"category_scores_gemma":[0.003092262,0.0004157729,0.0008801373,0.0006552063,0.0004570619,0.0009253236,0.0008466327,0.0007407647,0.0002667684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003844747,"about_ca_system_score_gemma":0.000580006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003034671,"about_ca_topic_score_gemma":0.003639848,"domain_scores_codex":[0.9994355,0.0001615521,0.00002675203,0.0001039928,0.0002301896,0.00004190138],"domain_scores_gemma":[0.9990757,0.000507566,0.00007419891,0.0001109554,0.0001939906,0.00003760866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003819668,0.0000688205,0.001432789,0.0001178279,0.00007456516,0.0001306994,0.0001635002,0.6980915,0.0470315,0.009542801,0.0007638713,0.2422003],"study_design_scores_gemma":[0.00000892229,0.00002461511,0.0002220525,0.00000295716,0.00001133051,0.00003658296,0.000007935763,0.9906636,0.006784463,0.001699747,0.0005305472,0.000007237981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009294104,0.00005516164,0.9899823,0.00003232192,0.000008795847,0.00002044719,0.00002983735,0.0001979157,0.0003790439],"genre_scores_gemma":[0.2942949,0.0002074482,0.7036557,0.00009723289,0.00006849474,0.00008714214,0.0003980041,0.0001799154,0.001011098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003034671,"threshold_uncertainty_score":0.008041441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01213343274169384,"score_gpt":0.2206527206800572,"score_spread":0.2085192879383634,"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."}}