{"id":"W2099609584","doi":"10.1109/igarss.2005.1525342","title":"Nonlinear feature extraction of hyperspectral data based on locally linear embedding (LLE)","year":2005,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; University of Victoria","funders":"","keywords":"Hyperspectral imaging; Feature extraction; Nonlinear system; Embedding; Pattern recognition (psychology); Computer science; Artificial intelligence; Extraction (chemistry); Remote sensing; Geology; Physics; Chemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.0001557752,0.0001413647,0.0001409167,0.0001125532,0.00003021996,0.00002391338,0.0002086644,0.0001166671,0.00006517461],"category_scores_gemma":[0.00009365887,0.0001362811,0.00003913053,0.0001662466,0.00002588186,0.0002657332,0.00001759584,0.0002537389,0.00008786577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001019241,"about_ca_system_score_gemma":0.00002766938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006407205,"about_ca_topic_score_gemma":0.00001559162,"domain_scores_codex":[0.9991572,0.00001750401,0.0001979689,0.0002421542,0.0002119857,0.0001732212],"domain_scores_gemma":[0.999002,0.00007611081,0.00004295888,0.0007637982,0.00006156164,0.00005356179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003741222,0.0001045072,0.00002920918,0.00005039728,0.00002334437,0.000005349333,0.0000485003,0.5930054,0.3682125,0.00003535065,0.009309816,0.02913827],"study_design_scores_gemma":[0.0002299339,0.00002451445,0.0002669978,0.00003583767,0.00001352055,0.000006593287,0.00002927468,0.8903759,0.09677222,0.00000135092,0.01212127,0.0001225484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1308088,0.0001746988,0.8195954,0.004090684,0.0007207079,0.0004769446,0.00007384917,0.001427359,0.04263157],"genre_scores_gemma":[0.6122143,0.00001826092,0.3866228,0.00008285325,0.0003798892,4.983788e-7,0.0001419537,0.00004301908,0.000496418],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4814055,"threshold_uncertainty_score":0.5557383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02833792529402468,"score_gpt":0.2927361114257568,"score_spread":0.2643981861317321,"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."}}