{"id":"W2794276902","doi":"10.3390/rs10030394","title":"Landsat Super-Resolution Enhancement Using Convolution Neural Networks and Sentinel-2 for Training","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Environment and Climate Change Canada","funders":"Canadian Space Agency","keywords":"Computer science; Convolutional neural network; Remote sensing; Land cover; Artificial intelligence; Image resolution; Pattern recognition (psychology); Land use; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000953644,0.0005064153,0.0002471032,0.0003642576,0.0001756814,0.0003162324,0.0003955137,0.000397857,0.0009509325],"category_scores_gemma":[0.00118021,0.0002153466,0.0004426077,0.0003071621,0.0002521271,0.0008337155,0.0004122044,0.0004589584,0.0002798025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003502552,"about_ca_system_score_gemma":0.0003042404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005342921,"about_ca_topic_score_gemma":0.01206767,"domain_scores_codex":[0.9998065,0.00002967515,0.000010942,0.0000521152,0.00006595164,0.00003485321],"domain_scores_gemma":[0.9997469,0.00007282856,0.0000322257,0.00004822178,0.00008308244,0.0000168455],"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":[0.0009722774,0.0005314378,0.04771435,0.0002801389,0.0003256962,0.0003694686,0.0002459764,0.3291041,0.3075393,0.001598745,0.002510101,0.3088084],"study_design_scores_gemma":[0.0000194524,0.0003613315,0.0435241,0.00002102482,0.00007573544,0.0001705592,0.00008419107,0.8692376,0.08375784,0.0008322252,0.00188111,0.00003486834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.892152,0.0004184657,0.1012989,0.0002246684,0.00009100612,0.0001042889,0.0005520187,0.00107534,0.004083241],"genre_scores_gemma":[0.8834332,0.0001532009,0.1140167,0.00008669856,0.0000145961,0.00004166218,0.0007949327,0.00005282489,0.001406226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005342921,"threshold_uncertainty_score":0.01062363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04701934527246122,"score_gpt":0.3068762002095216,"score_spread":0.2598568549370603,"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."}}