{"id":"W2114194579","doi":"10.1109/tgrs.2011.2125974","title":"Simultaneous Denoising and Intrinsic Order Selection in Hyperspectral Imaging","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Noise reduction; Hyperspectral imaging; Singular value decomposition; Computer science; Noise (video); Dimensionality reduction; Dimension (graph theory); Pattern recognition (psychology); Minification; Mathematical optimization; Selection (genetic algorithm); Algorithm; Artificial intelligence; Mathematics; Image (mathematics)","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":[0.0001475746,0.0001628262,0.0001350845,0.0003338304,0.0002207907,0.00007971531,0.00004071173,0.00006393171,0.000002249358],"category_scores_gemma":[0.00001878019,0.000169099,0.00002090042,0.0005843597,0.0001692665,0.000273303,0.000001108559,0.0002804491,0.000004672491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001064939,"about_ca_system_score_gemma":0.00002169177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004681163,"about_ca_topic_score_gemma":0.0002702145,"domain_scores_codex":[0.9990348,0.00003037699,0.0001907548,0.0003168997,0.0001250792,0.0003020548],"domain_scores_gemma":[0.999665,0.00006974043,0.00002619639,0.0001071085,0.00005439518,0.00007760636],"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":[0.00001203436,0.00001048073,0.000009229582,0.00001528148,0.00000432767,0.00003112138,0.001185179,0.005374091,0.1078266,0.000002355993,9.145232e-7,0.8855284],"study_design_scores_gemma":[0.0001885833,0.0000243334,0.00112624,0.00008718258,0.00001450087,0.0004039983,0.0004437968,0.9512932,0.04610368,0.00009551675,0.00001927407,0.0001996934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4388153,0.00004493586,0.5603834,0.00003772881,0.0001724141,0.00008060227,4.511116e-7,0.0001355222,0.000329594],"genre_scores_gemma":[0.9108002,0.0001441047,0.08893239,0.00003727105,0.00002148253,4.269676e-8,1.816802e-7,0.00002255104,0.00004181236],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9459191,"threshold_uncertainty_score":0.6895659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01297791958946163,"score_gpt":0.2132404217403385,"score_spread":0.2002625021508768,"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."}}