{"id":"W2166916201","doi":"10.1109/igarss.2003.1293967","title":"Application of Gaussian Markov random field model to unsupervised classification in polarimetric SAR image","year":2004,"lang":"en","type":"article","venue":"","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Gallipoli Medical Research Foundation","keywords":"Pattern recognition (psychology); Artificial intelligence; Markov random field; Contextual image classification; Polarimetry; Computer science; Gaussian; Random field; Gaussian process; Classifier (UML); Principal component analysis; Synthetic aperture radar; Mathematics; Image (mathematics); Image segmentation; Physics; Scattering; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001582915,0.0002954485,0.0005587816,0.0006815815,0.0003018522,0.0005560618,0.0006740691,0.0006916522,0.0005453671],"category_scores_gemma":[0.003838385,0.0002554447,0.0005471944,0.0007254304,0.0008181354,0.000919322,0.0003555608,0.0006214449,0.0002560299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008333876,"about_ca_system_score_gemma":0.0005575678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007748692,"about_ca_topic_score_gemma":0.00561741,"domain_scores_codex":[0.9994103,0.0002520314,0.00002235279,0.0001218297,0.0001289514,0.00006459333],"domain_scores_gemma":[0.9980366,0.001306426,0.0002017175,0.0001599107,0.0002545228,0.00004091693],"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":[0.00009861214,0.00004594848,0.002996158,0.00005378657,0.0000497295,0.0001267119,0.0001444575,0.8805923,0.003228238,0.03026392,0.001284918,0.08111512],"study_design_scores_gemma":[0.000001748507,0.000006480929,0.0002868904,0.000002026119,0.00000259383,0.00001640425,0.000003615663,0.9946775,0.0002739144,0.004568546,0.0001558699,0.000004412463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03262373,0.0002643468,0.965754,0.0001931322,0.00002500968,0.00001863044,0.00005596929,0.0003500287,0.0007151832],"genre_scores_gemma":[0.8050229,0.0005682403,0.1912604,0.0001544102,0.0001239385,0.00009886889,0.0003043503,0.00008617644,0.002380837],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007748692,"threshold_uncertainty_score":0.0154072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009416835881736856,"score_gpt":0.237401099427174,"score_spread":0.2279842635454371,"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."}}