{"id":"W2083101719","doi":"10.1016/j.comgeo.2013.08.007","title":"Space efficient data structures for dynamic orthogonal range counting","year":2013,"lang":"en","type":"article","venue":"Computational Geometry","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Dalhousie University","funders":"Canada Research Chairs","keywords":"Range (aeronautics); Space (punctuation); Computer science; Dynamic range; Data structure; Mathematics; Algorithm; Computer vision; Engineering; Aerospace engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002830074,0.0001579293,0.0001615958,0.0001845957,0.0002718905,0.0003567762,0.001517081,0.00005574261,0.00007550536],"category_scores_gemma":[0.0001160036,0.0001382179,0.00004752417,0.0004351539,0.00004619528,0.0006421472,0.001067509,0.0001153693,0.00008636568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003513003,"about_ca_system_score_gemma":0.00009918288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002133558,"about_ca_topic_score_gemma":0.000001184927,"domain_scores_codex":[0.9983439,0.00002813968,0.0002511314,0.0005931872,0.0004868078,0.0002968672],"domain_scores_gemma":[0.9983157,0.0004761083,0.0001357419,0.000704014,0.0002675834,0.0001008626],"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":[0.00002328305,0.0002790624,0.002942984,0.0001738871,0.0001211812,0.00001112554,0.00024764,0.5248798,0.0003453011,0.1751297,0.04816595,0.2476802],"study_design_scores_gemma":[0.0003708928,0.0000211595,0.05119852,0.00001443212,0.000004582414,0.000014848,0.00001345956,0.9267609,0.000006996238,0.0188431,0.002579883,0.0001712942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06690363,0.0003316214,0.9309264,0.0005861706,0.000536671,0.0003347248,0.0001845186,0.0001139823,0.00008222083],"genre_scores_gemma":[0.5712059,0.000001916148,0.4276654,0.000252065,0.0001158983,0.00001948917,0.0006685238,0.00001101069,0.00005979737],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5043023,"threshold_uncertainty_score":0.5636362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02183194825706517,"score_gpt":0.2821673451106919,"score_spread":0.2603353968536267,"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."}}