{"id":"W3137123549","doi":"10.1109/bigdata50022.2020.9378008","title":"Data Reduction and Deep-Learning Based Recovery for Geospatial Visualization and Satellite Imagery","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Geospatial analysis; Scalability; Big data; Deep learning; Pipeline (software); Data mining; Transfer of learning; Reduction (mathematics); Visualization; Satellite imagery; Computer data storage; Data science; Artificial intelligence; Database; Remote sensing","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":[0.000627972,0.0006594367,0.0004608593,0.001110936,0.000378683,0.0006779358,0.00134511,0.0006097715,0.00161647],"category_scores_gemma":[0.002976753,0.0002965559,0.0007358845,0.001965347,0.000745938,0.001653656,0.001212408,0.001246725,0.0006013098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005918691,"about_ca_system_score_gemma":0.0008678293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004620358,"about_ca_topic_score_gemma":0.006155966,"domain_scores_codex":[0.9994788,0.00006864411,0.00003944648,0.00009481174,0.0002652939,0.00005308995],"domain_scores_gemma":[0.9990469,0.0002505262,0.0001037428,0.0003437898,0.0002286664,0.00002647016],"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":[0.000313376,0.0002028635,0.004394693,0.0003108429,0.0001527761,0.0003409043,0.000247451,0.2574129,0.0672036,0.019763,0.01810715,0.6315504],"study_design_scores_gemma":[0.00001116173,0.00004116042,0.001657492,0.00002193939,0.00001814996,0.0001352121,0.00008038271,0.9466813,0.03244535,0.0127328,0.006152586,0.00002249839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02983453,0.000558576,0.9654169,0.0006359185,0.00009601967,0.00005448776,0.0005276342,0.00167579,0.001200023],"genre_scores_gemma":[0.3471798,0.0008784889,0.6444496,0.0003351908,0.0001176739,0.0001650462,0.00361364,0.0002652404,0.002995406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004620358,"threshold_uncertainty_score":0.009186924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04036374060985162,"score_gpt":0.3084832076063301,"score_spread":0.2681194669964785,"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."}}