{"id":"W3194145758","doi":"10.1145/3469830.3470892","title":"SPRIG: A Learned Spatial Index for Range and kNN Queries","year":2021,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Spatial query; Data mining; Spatial database; Overhead (engineering); Index (typography); Spatial analysis; Interpolation (computer graphics); Range (aeronautics); Grid; Range query (database); Exploit; Function (biology); Process (computing); Multivariate interpolation; Information retrieval; Artificial intelligence; Sargable; Web search query; Geography; Remote sensing","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":[0.0009020886,0.001110477,0.001311541,0.002187536,0.0006398597,0.001694423,0.002638135,0.0008835571,0.003469676],"category_scores_gemma":[0.006734456,0.0003873504,0.000794381,0.004054036,0.0006187954,0.004973604,0.002430525,0.001209015,0.002427773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001096544,"about_ca_system_score_gemma":0.002537012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009837493,"about_ca_topic_score_gemma":0.01323156,"domain_scores_codex":[0.9986781,0.0001342684,0.0001406051,0.000251008,0.000663974,0.0001319601],"domain_scores_gemma":[0.9980825,0.0004387631,0.0001762136,0.0007359159,0.0004460868,0.0001206022],"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":[0.000677011,0.0005291321,0.009856946,0.0005312829,0.0001580713,0.0003103194,0.0003372802,0.1704524,0.01396626,0.01572331,0.05303634,0.7344217],"study_design_scores_gemma":[0.00005102584,0.0001487947,0.000814123,0.00001645664,0.00001960847,0.0001981668,0.0001102393,0.9744916,0.005957185,0.008777046,0.009383022,0.00003271417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03524693,0.001454402,0.927353,0.0003817665,0.0001648066,0.0003450706,0.003625002,0.02714777,0.004281254],"genre_scores_gemma":[0.2871992,0.0009351654,0.6939455,0.0003245152,0.0001474675,0.0003876761,0.01239778,0.0009865271,0.00367613],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009837493,"threshold_uncertainty_score":0.01956052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02307858082302644,"score_gpt":0.2549051005605764,"score_spread":0.23182651973755,"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."}}