{"id":"W4416286235","doi":"10.1109/trs.2025.3633309","title":"From Dual to Qual: A Feature-Analysis-Oriented Interpretable Polarization Feature Generative Mapping Model for SAR Oil Spill Detection","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Radar Systems","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Oil spill; Synthetic aperture radar; Feature (linguistics); Feature extraction; Segmentation; Generative grammar; Pattern recognition (psychology); Feature selection; Polarimetry","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0004870478,0.000682614,0.000840885,0.001105924,0.001375531,0.0003941712,0.0002755153,0.0006800599,0.0001313669],"category_scores_gemma":[0.00003016157,0.0007326104,0.0006973638,0.003630128,0.0001114937,0.0005621334,0.000008990938,0.0006772705,0.0001148962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001821677,"about_ca_system_score_gemma":0.0001153763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003253735,"about_ca_topic_score_gemma":0.00451794,"domain_scores_codex":[0.9958568,0.0003631243,0.0008825687,0.001498641,0.0007362398,0.000662652],"domain_scores_gemma":[0.9981952,0.0001437179,0.0003877205,0.0007507148,0.0001907701,0.0003318619],"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":[0.0004693198,0.0002274798,0.00001783388,0.0001128697,0.001590482,0.000001670835,0.004251971,0.7328747,0.2267463,0.00004789305,0.0006885701,0.03297096],"study_design_scores_gemma":[0.001204857,0.0002392464,0.0001572998,0.0002994911,0.00168142,0.000004086927,0.00271936,0.8949767,0.09076145,0.00003705484,0.00728485,0.0006341893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02906584,0.0002736567,0.9592336,0.001099273,0.006550506,0.001454637,0.001670257,0.0002111277,0.000441121],"genre_scores_gemma":[0.9511945,0.00005264388,0.007388666,0.0005369821,0.0002132683,0.0003471834,0.00009243481,0.00006402479,0.04011034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9518449,"threshold_uncertainty_score":0.9999245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01168764149890005,"score_gpt":0.2426279724619843,"score_spread":0.2309403309630842,"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."}}