{"id":"W2167361654","doi":"","title":"Urban Land-cover Mapping with High-resolution Spaceborne SAR Data","year":2010,"lang":"en","type":"article","venue":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Remote sensing; Land cover; Cover (algebra); Geography; Synthetic aperture radar; Cartography; Environmental science; Land use; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002298773,0.000265396,0.0001414635,0.001300124,0.0001240321,0.0004517105,0.0002625758,0.0001418936,0.0005390276],"category_scores_gemma":[0.0005943156,0.000122498,0.0002203715,0.00123486,0.0001378564,0.0004112812,0.000251577,0.0001337992,0.000264774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004858361,"about_ca_system_score_gemma":0.0004134653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0406164,"about_ca_topic_score_gemma":0.08207759,"domain_scores_codex":[0.9998374,0.00002435682,0.000008540972,0.00003567573,0.00007725701,0.00001674004],"domain_scores_gemma":[0.9998475,0.00002708902,0.00003032443,0.00002671664,0.00005881937,0.000009604729],"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":[0.0001643734,0.0002218233,0.1193077,0.0002416755,0.0001724259,0.0005568016,0.0005111521,0.1900995,0.07120457,0.001255188,0.002974282,0.6132905],"study_design_scores_gemma":[0.00002437516,0.0001171825,0.2891315,0.00003647488,0.0000853991,0.0002010257,0.0005154291,0.6830398,0.01972513,0.0009495071,0.006131046,0.00004316279],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8783962,0.0004616722,0.1117929,0.0001117981,0.00001491266,0.0001833366,0.00161849,0.0006807079,0.006740065],"genre_scores_gemma":[0.929158,0.0002706721,0.06730267,0.00001792135,0.00000861945,0.00003790197,0.001653855,0.00001914403,0.001531274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0406164,"threshold_uncertainty_score":0.08075994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02342761158992603,"score_gpt":0.2320102456868118,"score_spread":0.2085826340968857,"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."}}