{"id":"W3126693333","doi":"10.1080/07038992.2020.1865141","title":"Deep Learning-Based Spatiotemporal Fusion Approach for Producing High-Resolution NDVI Time-Series Datasets","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sensor fusion; Remote sensing; Fuse (electrical); Computer science; Temporal resolution; Field (mathematics); Satellite; Scale (ratio); Normalized Difference Vegetation Index; Earth observation; Fusion; Land cover; Key (lock); Deep learning; Time series; Data mining; Artificial intelligence; Geography; Machine learning; Cartography; Land use; Climate change; Mathematics; Geology; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005755639,0.0001995717,0.0002817442,0.0001045027,0.0004961267,0.0001370846,0.0001461067,0.0001411252,0.00007599228],"category_scores_gemma":[0.0007093238,0.0001799623,0.0001199472,0.0003684698,0.0001491786,0.0002955726,0.00003195708,0.000389173,0.0000216752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005983603,"about_ca_system_score_gemma":0.0003329128,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006421767,"about_ca_topic_score_gemma":0.02178297,"domain_scores_codex":[0.9982964,0.0001840516,0.0003958358,0.0003473113,0.0003276136,0.0004488048],"domain_scores_gemma":[0.9987235,0.0000494228,0.0003737102,0.0002741507,0.0001397017,0.0004395375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009871393,0.00002417048,0.0005945522,0.00007250182,0.00005411497,0.0007766689,0.001023063,0.4468878,0.05602225,0.000003719297,0.02010169,0.4743408],"study_design_scores_gemma":[0.001101373,0.0002885287,0.008661777,0.000367378,0.0001403756,0.002552644,0.0004827292,0.8373106,0.03750362,0.0002170406,0.1106647,0.0007093261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3047449,0.0004079556,0.6886155,0.003180281,0.001041211,0.0005143018,0.00004088436,0.00004682221,0.001408109],"genre_scores_gemma":[0.2624244,0.000006396147,0.7360454,0.0002156319,0.0003765152,1.514433e-8,0.0003993066,0.00003588443,0.0004964613],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4736315,"threshold_uncertainty_score":0.9960669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009097760706611856,"score_gpt":0.1977372165296325,"score_spread":0.1886394558230207,"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."}}