{"id":"W3205581890","doi":"10.3390/rs13204102","title":"Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Endmember; Hyperspectral imaging; Anomaly detection; Pattern recognition (psychology); Sparse approximation; Computer science; Pixel; Artificial intelligence; Representation (politics); Spectral signature; Anomaly (physics); Remote sensing; Physics; Geography","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":[0.00009832482,0.000132518,0.0002058669,0.0001937125,0.00009589406,0.00009822365,0.00002417691,0.00009101764,0.000002057357],"category_scores_gemma":[0.0001822026,0.0001566784,0.0001110779,0.0006297604,0.00003364649,0.0001067058,0.00001136371,0.0001156717,0.000001859217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001145397,"about_ca_system_score_gemma":0.00001547329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004473087,"about_ca_topic_score_gemma":0.0002978453,"domain_scores_codex":[0.9991229,0.00004878547,0.0002013385,0.000306107,0.0001168921,0.000204],"domain_scores_gemma":[0.9993866,0.00008973303,0.00004613363,0.0002818097,0.0001350535,0.00006068509],"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":[0.00001053552,0.000001304397,0.00003710491,0.00002202503,0.0001529856,0.00001117234,0.0001856403,0.01082374,0.7438101,0.000004231105,0.000006654669,0.2449345],"study_design_scores_gemma":[0.0001596333,0.000005715241,0.00725579,0.000008242425,0.0002137349,0.00007168585,0.0001192648,0.6553998,0.3363249,0.000270043,0.00006244904,0.0001086862],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5463115,0.00007623633,0.4529935,0.00006181622,0.0001410062,0.00008297484,0.000001924334,0.0001260295,0.0002050253],"genre_scores_gemma":[0.8450727,0.00003083794,0.1546336,0.00001293303,0.00012969,1.511861e-8,0.00003659677,0.00002890465,0.00005472695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6445761,"threshold_uncertainty_score":0.6389162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02253133342749227,"score_gpt":0.2552358408634938,"score_spread":0.2327045074360015,"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."}}