{"id":"W2593159659","doi":"","title":"Base64Geo: an efficient data structure and transmission format for large, dense, scalar GIS datasets","year":2016,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Scalar (mathematics); Computer science; Data structure; Grid; String (physics); Magnitude (astronomy); Data mining; Tree (set theory); Database; Algorithm; Mathematics; Geometry; Physics; Combinatorics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00213691,0.001834493,0.001498684,0.003906892,0.00112844,0.003919497,0.004354364,0.001239069,0.02853861],"category_scores_gemma":[0.01429652,0.001428331,0.001366904,0.009602162,0.0007589083,0.006977513,0.004703004,0.0021713,0.02324484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001495845,"about_ca_system_score_gemma":0.002745744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006711183,"about_ca_topic_score_gemma":0.007119746,"domain_scores_codex":[0.9980427,0.0002353535,0.0003075897,0.0002835309,0.0008923478,0.0002385057],"domain_scores_gemma":[0.9945747,0.001090417,0.0004145942,0.002324708,0.001317457,0.0002780854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001085906,0.0001464618,0.004848739,0.001048171,0.0001429791,0.0004159302,0.0006076539,0.00925621,0.008200455,0.02022725,0.793255,0.1607651],"study_design_scores_gemma":[0.0005287415,0.0001473572,0.003853568,0.0003100057,0.00006080723,0.0004483493,0.0005723514,0.03828968,0.02549888,0.04790799,0.8821033,0.0002789493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.01412975,0.0009739018,0.2972695,0.00137564,0.0008464162,0.0007886693,0.3853084,0.2805933,0.01871438],"genre_scores_gemma":[0.05948694,0.001029577,0.2923906,0.0007283536,0.0001807477,0.001895336,0.6067626,0.02955063,0.007975237],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02853861,"threshold_uncertainty_score":0.0954712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01280190071441449,"score_gpt":0.231854452762741,"score_spread":0.2190525520483265,"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."}}