{"id":"W2132083814","doi":"10.1109/igarss.2008.4779288","title":"Estimating Dimensionality of Hyperspectral Data Using False Neighbour Method","year":2008,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; University of Victoria","funders":"Natural Resources Canada; National Aeronautics and Space Administration","keywords":"Hyperspectral imaging; Curse of dimensionality; Pattern recognition (psychology); Pixel; Computer science; Artificial intelligence; Land cover; Nonlinear system; Dimensionality reduction; Remote sensing; Mathematics; Data mining; Geography; Land use","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002852827,0.0001085284,0.0001790356,0.00005446635,0.00005871929,0.00001022354,0.0001858983,0.00004884017,0.00002989864],"category_scores_gemma":[0.00020127,0.0001055662,0.00003094144,0.0001617348,0.00004745787,0.0002494587,0.00006868115,0.0001033187,0.00001014092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005689507,"about_ca_system_score_gemma":0.00003030001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001110014,"about_ca_topic_score_gemma":0.000002837804,"domain_scores_codex":[0.9991407,0.00004131473,0.0002729695,0.000208029,0.0001766768,0.0001602687],"domain_scores_gemma":[0.9990271,0.0001170489,0.00005063252,0.0006956476,0.00006209449,0.0000474195],"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":[0.000004315306,0.00003053123,0.0005993052,0.00005887221,0.00004549215,0.00001336505,0.0001970592,0.1805306,0.8119842,0.0004968397,0.0005950524,0.005444375],"study_design_scores_gemma":[0.0001048157,0.000003585616,0.00391294,0.00001736634,0.00001549825,0.00009566765,0.0000259511,0.9584931,0.03700378,0.0001756971,0.00004332596,0.0001082859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4173676,0.00005251392,0.5808853,0.00003241729,0.0001391418,0.00005386233,0.000007499464,0.0001550894,0.001306563],"genre_scores_gemma":[0.4346161,0.000002186199,0.5652748,0.000008900583,0.00004220893,1.005704e-7,0.00001349096,0.00001555194,0.00002662255],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7779624,"threshold_uncertainty_score":0.4304863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1317369693003696,"score_gpt":0.3355495147851595,"score_spread":0.2038125454847899,"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."}}