{"id":"W2897972190","doi":"10.1145/3282825.3282830","title":"Improving Parallel Performance of Temporally Relevant Top-K Spatial Keyword Search","year":2018,"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 New Brunswick","funders":"","keywords":"Computer science; Exploit; Ranking (information retrieval); Relevance (law); Information retrieval; Keyword search; Similarity (geometry); Parallelism (grammar); Data mining; Artificial intelligence; Parallel computing","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":[0.001357801,0.001099587,0.001515373,0.001414842,0.0009949503,0.001690857,0.001887152,0.0005869606,0.003330817],"category_scores_gemma":[0.006935928,0.0004679438,0.0005590183,0.004159763,0.0004708471,0.003044555,0.001500368,0.000653062,0.001644737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001060711,"about_ca_system_score_gemma":0.003508283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01232035,"about_ca_topic_score_gemma":0.0141663,"domain_scores_codex":[0.9981197,0.0003062339,0.0002123657,0.0003895502,0.0005897934,0.0003823121],"domain_scores_gemma":[0.996843,0.001214367,0.0001989138,0.0006745139,0.0008144024,0.0002548418],"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":[0.00726927,0.001165244,0.01462114,0.001282805,0.0003717698,0.0006957215,0.0006767477,0.2524752,0.08215,0.01076286,0.03844369,0.5900856],"study_design_scores_gemma":[0.0003003179,0.0003429542,0.001333083,0.00001404046,0.00008339091,0.0002185197,0.0001791901,0.9651024,0.02074829,0.007210178,0.004438564,0.00002914071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6116288,0.009137126,0.3386938,0.0009726911,0.0007001613,0.0003313155,0.002470178,0.01931066,0.0167553],"genre_scores_gemma":[0.81659,0.001424319,0.174401,0.0001368707,0.0001581309,0.0001486086,0.002864073,0.0004425216,0.003834659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01232035,"threshold_uncertainty_score":0.02449727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01721448864260388,"score_gpt":0.2431786789543769,"score_spread":0.225964190311773,"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."}}