{"id":"W2057768602","doi":"10.1117/12.865035","title":"Correction of cardinal effects in high resolution SAR imagery","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Effigis (Canada); École de Technologie Supérieure","funders":"Canadian Space Agency; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Obstacle; Remote sensing; Azimuth; Position (finance); Artificial intelligence; Pixel; Computer vision; Orientation (vector space); Aerial imagery; Synthetic aperture radar; Radar imaging; Geology; Radar; Geography; Telecommunications; Mathematics; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001777825,0.0008769521,0.0005766818,0.001334269,0.0004948142,0.001147051,0.0008423654,0.0005365829,0.00136314],"category_scores_gemma":[0.004380352,0.000647955,0.000451673,0.001710492,0.0006465412,0.001219977,0.001243188,0.001411243,0.0007597897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002866966,"about_ca_system_score_gemma":0.0008480327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002108468,"about_ca_topic_score_gemma":0.005536231,"domain_scores_codex":[0.998806,0.0002704402,0.0000767377,0.0002303746,0.000479121,0.000137337],"domain_scores_gemma":[0.9971216,0.0005672692,0.0004916717,0.001024567,0.0006490024,0.0001458226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00118913,0.0003840458,0.07315851,0.0007827368,0.0002968781,0.000790768,0.0007399291,0.0797957,0.3907424,0.006494391,0.005528849,0.4400966],"study_design_scores_gemma":[0.000120203,0.0004639664,0.1841664,0.0001380374,0.0003133623,0.002672713,0.000589258,0.5233884,0.2708717,0.005186452,0.01188254,0.0002069565],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4433512,0.001707137,0.5464745,0.0002671858,0.0002337012,0.0001283247,0.0005280465,0.00296384,0.004345979],"genre_scores_gemma":[0.7676427,0.0008526507,0.2276149,0.00007953785,0.00006499254,0.0000408883,0.0009676774,0.0002497058,0.002486957],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002108468,"threshold_uncertainty_score":0.009402156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007228767468959866,"score_gpt":0.2123201827987958,"score_spread":0.205091415329836,"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."}}