{"id":"W2277068403","doi":"10.3390/rs8030187","title":"ℓ0-Norm Sparse Hyperspectral Unmixing Using Arctan Smoothing","year":2016,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Hyperspectral imaging; Smoothing; Norm (philosophy); Mathematical optimization; Computer science; Algorithm; Mathematics; Artificial intelligence; Statistics","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":[0.0003053398,0.0003340337,0.0003121478,0.000253109,0.0001897431,0.0001306038,0.0001337438,0.000165972,0.00001089304],"category_scores_gemma":[0.0002140457,0.0002961101,0.0001270761,0.0003381793,0.0001056781,0.0003920599,0.000042534,0.0002655301,0.0001111758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007611341,"about_ca_system_score_gemma":0.00004509706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007060794,"about_ca_topic_score_gemma":0.00001702079,"domain_scores_codex":[0.9980863,0.00006488866,0.0004241945,0.0004233842,0.0003157553,0.0006854823],"domain_scores_gemma":[0.9988852,0.0001484025,0.0000945505,0.0006011116,0.0001074679,0.0001632875],"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":[0.000005024175,0.000002441743,0.000008481495,0.00002341643,0.0000250154,0.00005498172,0.0002420976,0.002556478,0.7569364,0.0000272007,0.00005278365,0.2400657],"study_design_scores_gemma":[0.0003444623,0.000009921689,0.0001677624,0.0005596551,0.00003999286,0.0003666038,0.0001225902,0.8550649,0.1410502,0.0005671732,0.0012372,0.0004695617],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5608108,0.0001278654,0.4321016,0.0003477782,0.0007376331,0.000132199,0.000001544248,0.0008164776,0.00492409],"genre_scores_gemma":[0.7818379,0.00004493194,0.2173288,0.00004823573,0.0004446784,4.335162e-9,0.000001968771,0.0001328499,0.0001606197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8525084,"threshold_uncertainty_score":0.9999491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02887641818431834,"score_gpt":0.2375552101680296,"score_spread":0.2086787919837113,"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."}}