{"id":"W2052541749","doi":"10.1109/igarss.2014.6946718","title":"Integration of optical and polarimetric SAR imagery for locally accurate crop classification","year":2014,"lang":"en","type":"article","venue":"","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Polarimetry; Remote sensing; Synthetic aperture radar; Radar imaging; Computer science; Radar; Polarization (electrochemistry); Environmental science; Geology; Telecommunications; Optics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001694293,0.00008330915,0.0001175464,0.0001056042,0.00002783702,0.00002151205,0.00006577915,0.00008105258,0.00001236579],"category_scores_gemma":[0.0001144191,0.00006826504,0.00003021482,0.0001553216,0.00004320843,0.00007188442,0.000009533705,0.00005492648,0.000003119032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001885666,"about_ca_system_score_gemma":0.000006991675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001528609,"about_ca_topic_score_gemma":0.000002801293,"domain_scores_codex":[0.9995343,0.000007836139,0.000194812,0.0001149456,0.00005890759,0.00008916387],"domain_scores_gemma":[0.9994959,0.0001927504,0.00002897668,0.0001720935,0.00007623388,0.00003400444],"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":[0.000005134183,0.00002073668,0.00004131901,0.00003377674,0.0000100086,2.139544e-8,0.00001664903,0.000002472781,0.08130085,0.09658655,0.0007718012,0.8212107],"study_design_scores_gemma":[0.0002630242,0.0001094222,0.008102425,0.00003178868,0.0000437439,0.000006447036,0.00005601649,0.4416465,0.4106422,0.006036433,0.1328099,0.0002521201],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007392117,0.00007511453,0.9838936,0.0002061489,0.00002498941,0.0002304153,0.000004638848,0.0001854758,0.007987457],"genre_scores_gemma":[0.5707211,0.00002516093,0.4291444,0.00001966798,0.00002095776,0.00001127514,0.000008936776,0.00001060176,0.0000379688],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8209586,"threshold_uncertainty_score":0.2783767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01709764558188531,"score_gpt":0.2536992376537731,"score_spread":0.2366015920718877,"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."}}