{"id":"W2139736822","doi":"10.1109/igarss.1996.516864","title":"Textural processing of multi-polarization SAR for agricultural crop classification","year":2002,"lang":"en","type":"article","venue":"","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; York University","funders":"","keywords":"Artificial intelligence; Synthetic aperture radar; Pattern recognition (psychology); Computer science; Gray level; Pixel; Contextual image classification; Texture (cosmology); Classifier (UML); Speckle pattern; Image texture; Co-occurrence matrix; Image processing; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000317901,0.0003632601,0.0001670433,0.001175042,0.0001514713,0.000455434,0.0002190457,0.000165753,0.001565562],"category_scores_gemma":[0.001894859,0.00009328495,0.0002453386,0.001060681,0.0001867804,0.0004150178,0.0001387493,0.0002191433,0.0008709747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002913906,"about_ca_system_score_gemma":0.0002690692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002341976,"about_ca_topic_score_gemma":0.003975165,"domain_scores_codex":[0.9998456,0.00003792077,0.000008197781,0.00001804982,0.0000764259,0.000013771],"domain_scores_gemma":[0.9993308,0.0002130001,0.00008925008,0.00006091984,0.0002847748,0.0000214287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002169605,0.0000979479,0.009081809,0.0001725751,0.00003064479,0.00009181551,0.00007649053,0.0328342,0.1422785,0.001425239,0.002147776,0.811546],"study_design_scores_gemma":[0.00003374551,0.0003109899,0.07561468,0.00005007919,0.00007376225,0.0004731811,0.0002825511,0.7871025,0.1186626,0.005686443,0.01165437,0.00005504605],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2876143,0.0007389513,0.700525,0.0004603577,0.0001033272,0.000164992,0.001099997,0.002242798,0.007050241],"genre_scores_gemma":[0.590749,0.0006367153,0.4038421,0.00008740391,0.00007625511,0.0001172761,0.001247647,0.0001335728,0.003110051],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002341976,"threshold_uncertainty_score":0.005237401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03942033683672279,"score_gpt":0.2503529934420806,"score_spread":0.2109326566053578,"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."}}