{"id":"W2913607312","doi":"10.1109/lgrs.2019.2895629","title":"Subpixel Mapping Based on Hopfield Neural Network With More Prior Information","year":2019,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Fundamental Research Funds for the Central Universities; National Aerospace Science Foundation of China; National Natural Science Foundation of China","keywords":"Subpixel rendering; Computer science; Hyperspectral imaging; Pixel; Artificial neural network; Artificial intelligence; Cover (algebra); Image (mathematics); Land cover; Pattern recognition (psychology); Path (computing); Image resolution; Computer vision; Land use","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":[0.0001815412,0.0001774558,0.0001453893,0.000153273,0.0001480364,0.000176595,0.0000853942,0.00006497865,9.692345e-7],"category_scores_gemma":[0.00001824462,0.0001500241,0.00002959879,0.0003741565,0.0001197714,0.0004424548,0.000008921548,0.000224535,0.00002326923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005363379,"about_ca_system_score_gemma":0.00001765602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003924828,"about_ca_topic_score_gemma":0.000005102861,"domain_scores_codex":[0.9989262,0.0000216334,0.0001864294,0.0002197195,0.0002804332,0.0003656422],"domain_scores_gemma":[0.9994433,0.00007036485,0.00006645738,0.0003111689,0.00003869398,0.00007003772],"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":[0.00002823274,0.000002642829,0.0002541413,0.00009349958,0.000007776968,0.00001723353,0.0006072612,0.5952965,0.09677606,0.000001818834,0.001172233,0.3057426],"study_design_scores_gemma":[0.0002481602,0.00003082302,0.007905915,0.0001697665,0.000005921415,0.00003645683,0.00006870755,0.98873,0.00137797,0.000002862392,0.00120824,0.0002151804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7635952,0.000005187942,0.2320141,0.002842552,0.0007400147,0.00018955,5.946016e-7,0.0001833926,0.0004294002],"genre_scores_gemma":[0.9531043,0.000004257149,0.0414468,0.005268245,0.0001131244,4.7299e-8,0.000005684651,0.00001974901,0.00003783259],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3934335,"threshold_uncertainty_score":0.6117806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007447326311286747,"score_gpt":0.1848755595556099,"score_spread":0.1774282332443231,"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."}}