{"id":"W4247603740","doi":"10.32920/14638896","title":"A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National High-tech Research and Development Program; National Natural Science Foundation of China","keywords":"Computer science; Search engine indexing; Tree (set theory); Data mining; Hash table; Database; Hash function; Trajectory; Access method; NoSQL; R-tree; Temporal database; Data structure; Database index; Table (database); Field (mathematics); Spatial database; Big data; Information retrieval; Spatial analysis; Mathematics","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":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001508732,0.0003321917,0.000428871,0.000200838,0.000118571,0.001243808,0.005962738,0.00005706393,0.00009710707],"category_scores_gemma":[0.000164649,0.0003230175,0.0001204734,0.0001247032,0.00002501245,0.00209719,0.02208007,0.0003341696,0.00001010165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004168885,"about_ca_system_score_gemma":0.0003630873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008795017,"about_ca_topic_score_gemma":0.0004220182,"domain_scores_codex":[0.9967917,0.0001380023,0.000427994,0.001852106,0.0004157328,0.0003744698],"domain_scores_gemma":[0.994024,0.0002534248,0.0002341299,0.005289524,0.00009972634,0.00009916464],"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":[0.00001628348,0.0003498405,0.000518331,0.001201521,0.0005645541,0.0002297426,0.0002533628,0.000615145,0.00001918113,0.03267838,0.2638187,0.699735],"study_design_scores_gemma":[0.0002672654,0.00001476516,0.00009679262,0.0001079835,0.00005230639,0.000005945355,0.00004278652,0.8150381,0.0003689027,0.00106937,0.1824573,0.0004784368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00008581885,0.0002589787,0.9934903,0.0006031098,0.001748898,0.0006527391,0.001490886,0.0003408026,0.001328415],"genre_scores_gemma":[0.0009657542,0.00004514325,0.9649063,0.0006037047,0.0003684139,0.00008213893,0.03147091,0.0000245029,0.00153313],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.814423,"threshold_uncertainty_score":0.9999222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1307852154575284,"score_gpt":0.3587869863904408,"score_spread":0.2280017709329124,"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."}}