{"id":"W1563424464","doi":"10.1007/978-3-642-37487-6_17","title":"Indexing Reverse Top-k Queries in Two Dimensions","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Search engine indexing; Computer science; Polygon (computer graphics); Tuple; Information retrieval; Combinatorics; Theoretical computer science; Discrete mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001549234,0.0009257274,0.002801881,0.0024437,0.00169371,0.005217125,0.002977571,0.001927122,0.01457714],"category_scores_gemma":[0.01019707,0.0008479992,0.001241537,0.007428966,0.001796842,0.01023124,0.004510716,0.002638755,0.007084179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001525259,"about_ca_system_score_gemma":0.002259201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002343647,"about_ca_topic_score_gemma":0.00423588,"domain_scores_codex":[0.9958931,0.0005366727,0.0006503526,0.0005955301,0.001695379,0.0006289089],"domain_scores_gemma":[0.9916276,0.002006265,0.0002968487,0.004639693,0.001230836,0.0001988519],"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":[0.001809397,0.000723358,0.00416368,0.002150102,0.0002055282,0.0006423996,0.0009650735,0.02855754,0.03690728,0.1549294,0.1371779,0.6317683],"study_design_scores_gemma":[0.0004140516,0.0004024149,0.001924136,0.0002818783,0.0002579123,0.003821349,0.001361017,0.3353265,0.0502053,0.4785599,0.1272098,0.000235764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1768176,0.008115676,0.6967491,0.003985517,0.001778698,0.0007020928,0.01171433,0.02334421,0.0767928],"genre_scores_gemma":[0.4623479,0.002368796,0.4835871,0.001043654,0.0005418038,0.0003258609,0.01749932,0.002480191,0.02980542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01457714,"threshold_uncertainty_score":0.04876542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01701251877270165,"score_gpt":0.2537123457185366,"score_spread":0.2366998269458349,"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."}}