{"id":"W4362519254","doi":"10.3390/rs15071855","title":"Proof and Application of Discriminating Ocean Oil Spills and Seawater Based on Polarization Ratio Using Quad-Polarization Synthetic Aperture Radar","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography; Fisheries and Oceans Canada","funders":"National Key Research and Development Program of China; Canadian Space Agency; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Seawater; Oil spill; Environmental science; Synthetic aperture radar; Remote sensing; Polarization (electrochemistry); Meteorology; Petroleum engineering; Geology; Environmental engineering; Oceanography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002549617,0.0001141611,0.0001084045,0.00009974607,0.0002261682,0.00003869046,0.00002603527,0.00007904125,0.000005013965],"category_scores_gemma":[0.0001282985,0.0001080544,0.00002000238,0.0003480199,0.00009068993,0.000164304,0.00003436249,0.00007843476,0.000006526873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000754064,"about_ca_system_score_gemma":0.000007157538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003656384,"about_ca_topic_score_gemma":0.00003456717,"domain_scores_codex":[0.9990882,0.00008070889,0.0001935066,0.0002775776,0.0002216235,0.0001383367],"domain_scores_gemma":[0.9996057,0.00006418475,0.0001276194,0.0001340124,0.00001840297,0.00005009002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009063434,0.000006660153,0.0009830482,0.00005063384,0.000002214088,7.327245e-7,0.0002866648,0.005075837,0.7393381,0.00001355956,0.000002199519,0.2542313],"study_design_scores_gemma":[0.0001846059,0.00003087693,0.005773189,0.00009303849,0.00001814957,0.000007944194,0.0001045497,0.9253392,0.06814712,0.00009319196,0.0000929562,0.0001151643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8730728,0.000007256772,0.1259897,0.0003349682,0.00004859673,0.0001575799,0.000002417482,0.00006206902,0.0003245424],"genre_scores_gemma":[0.9931123,0.000005509145,0.006555127,0.0001382799,0.00002317188,3.750421e-8,0.00003840943,0.00002076121,0.0001063985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9202634,"threshold_uncertainty_score":0.4406331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008354672025275591,"score_gpt":0.2218359740271784,"score_spread":0.2134813020019028,"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."}}