{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001000691,0.00005679532,0.00007445477,0.00002813469,0.000062924,0.0002830557,0.00020884,0.00001777198,0.00004858087],"category_scores_gemma":[0.00002637405,0.00004921064,0.00002170786,0.0001000474,0.00002003454,0.0004584781,0.0003549776,0.00002704416,0.00001275308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003785771,"about_ca_system_score_gemma":0.0000189925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007233783,"about_ca_topic_score_gemma":0.000164499,"domain_scores_codex":[0.9994718,0.00001209106,0.00007108135,0.0002317634,0.00008769943,0.0001255222],"domain_scores_gemma":[0.9996503,0.00003226184,0.00001692993,0.0002354193,0.0000320194,0.00003306115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009502519,0.00004218278,0.00228576,0.00003891162,0.00002660342,0.00002846844,0.0002077793,0.000001486131,0.00009001212,0.3100112,0.00819481,0.6790633],"study_design_scores_gemma":[0.00253891,0.0001597306,0.02198631,0.00002027691,0.00001821378,0.00001322811,0.0002203246,0.2551141,0.003173621,0.02672432,0.6895306,0.0005003387],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001402108,0.00005955328,0.9900053,0.00316222,0.000191414,0.00009916,0.00000348725,0.00008043832,0.004996281],"genre_scores_gemma":[0.5268577,0.0001574617,0.4147041,0.003093273,0.0004383961,0.00007597991,0.00005358673,0.00002092948,0.05459848],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6813358,"threshold_uncertainty_score":0.2729512,"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."}}