{"id":"W2903819099","doi":"10.1049/iet-its.2018.5036","title":"Efficient processing of distance–time <i>k</i> th‐order skyline queries in bicriteria networks","year":2018,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Novelis (Canada)","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Skyline; Computer science; Order (exchange); Combinatorics; Mathematics; Data mining; Economics","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":[0.000584952,0.0006697914,0.001012489,0.001102499,0.0008791178,0.001275638,0.001459753,0.000770313,0.001337418],"category_scores_gemma":[0.002684382,0.0003507882,0.0004144544,0.002119994,0.0004857928,0.002697924,0.001102247,0.0004912663,0.0004592814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007939126,"about_ca_system_score_gemma":0.001012656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009994349,"about_ca_topic_score_gemma":0.01031101,"domain_scores_codex":[0.9989377,0.0001921155,0.0001190774,0.0002848904,0.0002796229,0.0001865948],"domain_scores_gemma":[0.9987139,0.0004776106,0.0001466963,0.0003094096,0.0002712728,0.0000811751],"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":[0.002019969,0.0003535062,0.01076637,0.0004938027,0.0001890271,0.0008913426,0.001235949,0.3587707,0.08254673,0.01758377,0.01647021,0.5086787],"study_design_scores_gemma":[0.00003508096,0.0001408794,0.001702541,0.000007944187,0.00001818894,0.0002887599,0.0004187275,0.9764109,0.0123564,0.005612132,0.002982365,0.00002608209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2885767,0.0008676919,0.7001803,0.0003679817,0.00005563281,0.0002394539,0.0008677912,0.003333817,0.005510593],"genre_scores_gemma":[0.7860429,0.0002751325,0.2096749,0.0001227336,0.00003209808,0.000124566,0.001778107,0.000112029,0.00183747],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009994349,"threshold_uncertainty_score":0.01987237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01206747924645435,"score_gpt":0.235288946789858,"score_spread":0.2232214675434036,"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."}}