{"id":"W3142102071","doi":"10.3390/rs13071253","title":"Hyperspectral Image Classification via Multi-Feature-Based Correlation Adaptive Representation","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Science and Technology Department of Henan Province","keywords":"Classifier (UML); Computer science; Pattern recognition (psychology); Artificial intelligence; Hyperspectral imaging; Correlation; Regularization (linguistics); Sparse approximation; Feature selection; Tikhonov regularization; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001651678,0.0002572901,0.0002394676,0.0001724751,0.0001529213,0.00013683,0.0000674248,0.0002116104,0.000008620204],"category_scores_gemma":[0.0002637467,0.0003121208,0.0001263453,0.0006261058,0.0000759648,0.0003273357,0.00001464058,0.0003863877,0.0001283455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004944111,"about_ca_system_score_gemma":0.00007609869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002925975,"about_ca_topic_score_gemma":0.00003112227,"domain_scores_codex":[0.998374,0.000161824,0.0003407638,0.0004829308,0.0003107677,0.0003297249],"domain_scores_gemma":[0.9986125,0.0001403322,0.0001247043,0.0005935344,0.0004290027,0.0000998992],"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":[0.00001735275,0.00001436557,0.00001162241,0.00002262633,0.00002625109,0.00005393136,0.0002016964,0.03294054,0.8487148,0.00002420652,0.000308078,0.1176645],"study_design_scores_gemma":[0.0004517366,0.000009986421,0.003161359,0.00007302541,0.00004400148,0.00008444125,0.0002115968,0.8176236,0.1777701,0.0001269241,0.0001867575,0.0002564985],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04024594,0.0001645625,0.953872,0.0005498377,0.0006959109,0.0002447549,0.000003508811,0.0006993914,0.003524061],"genre_scores_gemma":[0.5910279,0.00001969571,0.4083027,0.00004941012,0.0001499458,3.810583e-8,0.0001539775,0.00006804211,0.000228272],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.784683,"threshold_uncertainty_score":0.9999331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03548999507354676,"score_gpt":0.2663929774890574,"score_spread":0.2309029824155106,"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."}}