{"id":"W2777217533","doi":"10.1145/3139958.3140007","title":"Best-Compromise In-Route Nearest Neighbor Queries","year":2017,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Ciência sem Fronteiras; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Compromise; Skyline; Computer science; Context (archaeology); Point of interest; Point (geometry); k-nearest neighbors algorithm; Path (computing); Algorithm; Theoretical computer science; Mathematical optimization; Data mining; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00317245,0.00148512,0.002569236,0.001307357,0.001501397,0.002297287,0.003552779,0.002913961,0.004603476],"category_scores_gemma":[0.014221,0.0007631061,0.001217175,0.002637656,0.0009282226,0.004729448,0.00300135,0.001445041,0.001531007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001193855,"about_ca_system_score_gemma":0.0009937647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001527913,"about_ca_topic_score_gemma":0.003403386,"domain_scores_codex":[0.995526,0.001713468,0.0003385679,0.0009190344,0.001066293,0.0004365526],"domain_scores_gemma":[0.9944137,0.002969008,0.000424242,0.001358047,0.0005896829,0.0002454348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002058764,0.0005704571,0.003636797,0.0006762507,0.0003345006,0.0004846002,0.0009718054,0.6849453,0.01791978,0.03292721,0.02367684,0.2317977],"study_design_scores_gemma":[0.0000911048,0.0002514645,0.0004512921,0.00002163706,0.00004634679,0.0005728697,0.0003242881,0.9568213,0.005558948,0.03042349,0.005400567,0.00003665623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.077903,0.001030199,0.9111701,0.0008262687,0.0001126593,0.0002668578,0.0007996459,0.001461239,0.006430086],"genre_scores_gemma":[0.4501649,0.0003610963,0.5418656,0.0003096331,0.0001272875,0.0002428273,0.001995847,0.0004590828,0.004473609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004603476,"threshold_uncertainty_score":0.01677775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0294596867744005,"score_gpt":0.2821589006508625,"score_spread":0.252699213876462,"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."}}