{"id":"W2129733358","doi":"10.1109/igarss.2005.1526022","title":"Classification of the polarimetric SAR using fuzzy boundaries in entropy and alpha plane","year":2005,"lang":"en","type":"article","venue":"","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Polarimetry; Synthetic aperture radar; Entropy (arrow of time); Radar imaging; Fuzzy logic; Fuzzy set; Artificial intelligence; Scattering; Contextual image classification; Computer science; Pattern recognition (psychology); Mathematics; Remote sensing; Radar; Physics; Geography; Image (mathematics); Optics","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":[0.002108002,0.000408217,0.0005210862,0.002289837,0.0005136342,0.001639432,0.0004389481,0.0006276701,0.0008015453],"category_scores_gemma":[0.005074964,0.0001367897,0.0003659935,0.0007977278,0.001079656,0.00230839,0.0007368553,0.0004582553,0.0002393026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005371806,"about_ca_system_score_gemma":0.0002626362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005948977,"about_ca_topic_score_gemma":0.0003207597,"domain_scores_codex":[0.9991816,0.0001740023,0.00008370196,0.0001257778,0.0003726011,0.00006227717],"domain_scores_gemma":[0.9968594,0.001513881,0.0003410767,0.0002579462,0.00088838,0.0001393122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001430533,0.0001864157,0.03089107,0.0003500481,0.0001051825,0.0005000684,0.001357953,0.1495476,0.09661124,0.05851593,0.001917324,0.6585866],"study_design_scores_gemma":[0.00004090627,0.0002501391,0.01613928,0.00008879232,0.00004762311,0.0003170764,0.0003898607,0.9056382,0.03623724,0.03842866,0.002339387,0.00008286045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2725242,0.0003861947,0.7226073,0.000208515,0.00006978933,0.0001117818,0.0001676301,0.0002603122,0.003664318],"genre_scores_gemma":[0.8452417,0.0001367955,0.153793,0.00003746432,0.00004209268,0.00007174908,0.0001788747,0.00001712517,0.0004811953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002289837,"threshold_uncertainty_score":0.01114833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01250769213539208,"score_gpt":0.2309552058544257,"score_spread":0.2184475137190336,"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."}}