{"id":"W2125387481","doi":"10.1109/igarss.1989.576568","title":"Discrete Target Recognition In Polarimetric Sar Data","year":2005,"lang":"en","type":"article","venue":"","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Synthetic aperture radar; Polarimetry; Artificial intelligence; Computer vision; Remote sensing; Pattern recognition (psychology); Geology; Physics","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":[0.0002570199,0.0002365592,0.0003998886,0.0005246399,0.0001314141,0.0006535349,0.0003110933,0.0003813709,0.001464305],"category_scores_gemma":[0.00126834,0.0002203467,0.0002029492,0.0006740183,0.0003689299,0.0007016333,0.0004482874,0.0005528029,0.0007201868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001460863,"about_ca_system_score_gemma":0.000151931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005775542,"about_ca_topic_score_gemma":0.0008023871,"domain_scores_codex":[0.9997942,0.00004766647,0.00001047689,0.00004301122,0.00006969018,0.00003494271],"domain_scores_gemma":[0.9994262,0.0003332656,0.00004557519,0.00009914045,0.00006825094,0.0000275015],"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":[0.000949651,0.0001350696,0.005130202,0.0002369872,0.00004667001,0.0003500691,0.0001540871,0.1187957,0.200726,0.009768563,0.002838419,0.6608686],"study_design_scores_gemma":[0.00003413939,0.00008525049,0.005753009,0.00001269951,0.00002198572,0.0004096444,0.0001054309,0.9262384,0.05709969,0.008124897,0.002097001,0.00001793202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2098261,0.0003500585,0.7854218,0.0002412224,0.0000872404,0.00002307275,0.000364349,0.0005574657,0.003128785],"genre_scores_gemma":[0.8855234,0.0004127287,0.1094226,0.00009384834,0.00007170905,0.0000328016,0.0009032328,0.00007454378,0.003465251],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001464305,"threshold_uncertainty_score":0.004898608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02907214447072449,"score_gpt":0.2554132234274728,"score_spread":0.2263410789567483,"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."}}