{"id":"W2122098189","doi":"10.1109/iasp.2009.5054570","title":"Research on optimized spatial data query algorithm in the spatial database","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Object-based spatial database; Spatial database; Spatial analysis; Spatial query; Computer science; Data mining; Object (grammar); Spatiotemporal database; Database; Information retrieval; View; Database design; Sargable; Web search query; Artificial intelligence; Search engine; Database tuning; 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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.004079999,0.0001446732,0.0001469064,0.0003077742,0.0001776539,0.0006748572,0.006738208,0.00003870233,0.00008057182],"category_scores_gemma":[0.00009250848,0.00009455886,0.00002481225,0.0008211626,0.00005701268,0.001574201,0.001852928,0.0004290646,0.000251888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002612414,"about_ca_system_score_gemma":0.00006085337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002629586,"about_ca_topic_score_gemma":0.0003096327,"domain_scores_codex":[0.9969921,0.0004264746,0.0002487491,0.0007767038,0.001055679,0.0005003234],"domain_scores_gemma":[0.9959865,0.0003087237,0.00003770116,0.003556082,0.00004499266,0.00006596877],"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":[0.00001785491,0.0003134242,0.000006139381,0.000002332067,0.00000535542,0.0001788807,0.00008606552,0.00004824561,0.000007317698,0.02219844,0.0628638,0.9142721],"study_design_scores_gemma":[0.001036928,0.0002258094,0.001531991,0.00002182834,0.000003244545,0.000004306226,0.0001189979,0.9692821,0.00008507447,0.001541101,0.02595294,0.0001957004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001008189,0.00002211683,0.972227,0.008966397,0.0002806969,0.0004042446,0.00007463262,0.00009270803,0.01783132],"genre_scores_gemma":[0.09674866,0.0002046118,0.8888447,0.00817954,0.001449841,0.00004537272,0.002170726,0.00002228821,0.002334248],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9692338,"threshold_uncertainty_score":0.9986358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1349027993749875,"score_gpt":0.3855172972026257,"score_spread":0.2506144978276382,"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."}}