{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003659532,0.0003402212,0.0004274505,0.001028616,0.0001752055,0.0005002872,0.0002654215,0.0002732172,0.0004474368],"category_scores_gemma":[0.0007307376,0.0001274621,0.0004564127,0.0007468204,0.0001926488,0.0007490841,0.0002592475,0.0003720396,0.0002218445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002305503,"about_ca_system_score_gemma":0.0002008092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001248136,"about_ca_topic_score_gemma":0.001513345,"domain_scores_codex":[0.9997649,0.00004353729,0.0000141417,0.00004953302,0.0001067497,0.00002111416],"domain_scores_gemma":[0.9996411,0.00008837572,0.00005332755,0.00004345198,0.0001556853,0.00001809452],"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":[0.0003992861,0.0002654477,0.01415901,0.0001489912,0.0001557329,0.0001785105,0.0001065458,0.1033862,0.1251987,0.002500658,0.002222536,0.7512783],"study_design_scores_gemma":[0.000009422519,0.00007800632,0.009825886,0.0000048296,0.00002413467,0.00007541758,0.00002876611,0.9671941,0.02077821,0.0007990064,0.001164599,0.00001753333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2715841,0.0003984779,0.7252091,0.0001413367,0.00007251011,0.00004833652,0.0001842335,0.0008742902,0.001487645],"genre_scores_gemma":[0.8196275,0.0002471565,0.1777526,0.00003260408,0.00005088183,0.00004485349,0.0004853916,0.00004123075,0.00171776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001248136,"threshold_uncertainty_score":0.002481699,"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."}}