{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001423413,0.0009008092,0.0007933812,0.0008214219,0.0003731815,0.0007694418,0.001245235,0.0009960007,0.001343074],"category_scores_gemma":[0.00256636,0.0005548424,0.001030569,0.001070285,0.0008117402,0.001492168,0.001092591,0.001490993,0.000588175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000708891,"about_ca_system_score_gemma":0.001268275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002810955,"about_ca_topic_score_gemma":0.003241668,"domain_scores_codex":[0.9994978,0.00009897343,0.00003060541,0.0001422325,0.0001999576,0.00003039668],"domain_scores_gemma":[0.999333,0.0002830121,0.00009747311,0.0001091851,0.0001530342,0.00002423777],"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":[0.000189779,0.000100854,0.0009224263,0.000199999,0.0001167227,0.00008291772,0.0002678305,0.6043421,0.03600072,0.02745975,0.0023894,0.3279275],"study_design_scores_gemma":[0.000005479979,0.00001376804,0.0001001435,0.000003868601,0.0000062852,0.00002460749,0.000006693753,0.9906308,0.004977678,0.002968988,0.001252052,0.000009758282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005970617,0.00007179435,0.9930344,0.00005888349,0.00001193203,0.00001670064,0.00001397416,0.0003122961,0.0005094663],"genre_scores_gemma":[0.09719277,0.0001868878,0.8998639,0.0000705586,0.00003096139,0.00008519966,0.0001641139,0.0001754965,0.002230108],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002810955,"threshold_uncertainty_score":0.007527828,"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."}}