{"id":"W2745791577","doi":"10.3390/rs9090872","title":"Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification","year":2017,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Università degli Studi di Pavia; National Natural Science Foundation of China; Jet Propulsion Laboratory; Purdue University; National Aeronautics and Space Administration","keywords":"Pattern recognition (psychology); Sparse approximation; Computer science; Hyperspectral imaging; Pixel; Artificial intelligence; Classifier (UML); Representation (politics)","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":[0.0002603607,0.000219036,0.0002247158,0.0001302014,0.0005902249,0.0002800976,0.0001694973,0.0001483873,0.000001523138],"category_scores_gemma":[0.0004623836,0.0002521239,0.0001251456,0.00009970942,0.0001694168,0.0005520732,0.00002746861,0.0001975889,0.00004522095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002837826,"about_ca_system_score_gemma":0.00002540914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001040558,"about_ca_topic_score_gemma":0.00005979633,"domain_scores_codex":[0.9986768,0.00006621412,0.0003104465,0.000412051,0.0001874131,0.0003470791],"domain_scores_gemma":[0.9982522,0.0001188294,0.0002097034,0.001078121,0.0002435482,0.00009763698],"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":[0.00002845373,0.000009456621,0.000008121304,0.00002920428,0.00003367977,0.00001010487,0.0003786509,0.00189048,0.7620999,0.0003075839,0.001335355,0.233869],"study_design_scores_gemma":[0.0004784981,0.00001994514,0.005028305,0.00009488595,0.00004597797,0.00004089996,0.0003366049,0.9328837,0.0586762,0.001333709,0.0007995431,0.000261675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1122869,0.00003895316,0.8762372,0.001303112,0.000727014,0.0006044591,0.000008797066,0.0005013255,0.008292233],"genre_scores_gemma":[0.7398562,0.00005134397,0.2593021,0.00001261571,0.0003126501,1.181625e-7,0.00004470818,0.00007255242,0.0003476173],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9309933,"threshold_uncertainty_score":0.9999931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0771561299403723,"score_gpt":0.3075723505921109,"score_spread":0.2304162206517386,"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."}}