{"id":"W2373523947","doi":"","title":"Land Cover Classification of Hyperspectral Data Using Composite Kernel Support Vector Machines","year":2011,"lang":"en","type":"article","venue":"Beijing Daxue xuebao. Ziran kexue ban","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Support vector machine; Hyperspectral imaging; Kernel (algebra); Pattern recognition (psychology); Land cover; Artificial intelligence; Kernel method; Radial basis function kernel; Computer science; Mathematics; Remote sensing; Data mining; Land use; Geography; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000363011,0.0003315666,0.0003992378,0.0002173561,0.0001153962,0.00008117093,0.0005826193,0.000167078,0.00009880056],"category_scores_gemma":[0.00009186378,0.0003554825,0.00008519891,0.0003473195,0.0001426547,0.0006025249,0.00008570449,0.0002753803,0.00008051212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001494509,"about_ca_system_score_gemma":0.00006882641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001808751,"about_ca_topic_score_gemma":0.00003580415,"domain_scores_codex":[0.9980791,0.00008951526,0.0005912629,0.0005212538,0.0003103273,0.000408546],"domain_scores_gemma":[0.9980774,0.00006607828,0.0002003515,0.001415312,0.0001119052,0.0001289436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006726864,0.0001452944,0.04070253,0.0002601052,0.000167024,0.0000238341,0.001061807,0.002363378,0.9509159,0.000462418,0.001358409,0.002472069],"study_design_scores_gemma":[0.001223201,0.00007540354,0.2444888,0.0001878646,0.0002837069,0.0001393139,0.00009812802,0.7035267,0.04577499,0.00008834307,0.003225328,0.0008882296],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9707238,0.0002084425,0.01853155,0.0000499114,0.0008427906,0.0003567097,0.0001792684,0.0003866584,0.008720876],"genre_scores_gemma":[0.975124,0.00002117758,0.02399866,0.0000227117,0.0001631285,0.000002474673,0.0003469255,0.0001169554,0.0002039697],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9051409,"threshold_uncertainty_score":0.9998897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09809643875919695,"score_gpt":0.2744029725501956,"score_spread":0.1763065337909986,"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."}}