{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009072103,0.0001743891,0.0001449256,0.0001836643,0.0003207838,0.0005817109,0.001844066,0.00003579776,0.000001864741],"category_scores_gemma":[0.00008696716,0.0001204914,0.00001351694,0.0003480493,0.00007067766,0.003347808,0.001585788,0.00005626845,0.000001099672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001859796,"about_ca_system_score_gemma":0.00004041332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001939179,"about_ca_topic_score_gemma":9.717204e-7,"domain_scores_codex":[0.9982205,0.00001115047,0.000164263,0.0007943377,0.0003556119,0.0004541215],"domain_scores_gemma":[0.9984815,0.000117802,0.00003536447,0.001052812,0.00006452046,0.0002480169],"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":[0.000003388419,0.00003324243,0.0002636601,0.000101076,0.000009133613,0.00001106425,0.0002306839,0.0006326954,0.0005962492,0.003355865,0.001993315,0.9927696],"study_design_scores_gemma":[0.000421391,0.00006280784,0.001881526,0.00007248165,0.000006269468,0.00001927922,0.000002370424,0.9702404,0.000403373,0.00008547816,0.02657409,0.0002305286],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01349613,0.0001574459,0.9849341,0.0001777328,0.0003674973,0.0001996594,0.0004732974,0.0001936457,4.745281e-7],"genre_scores_gemma":[0.09359746,0.00004281014,0.9058692,0.0001909562,0.0001200002,0.000005476657,0.0001557161,0.00001214902,0.000006257465],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9925391,"threshold_uncertainty_score":0.5609452,"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."}}