{"id":"W2295356180","doi":"10.1145/2830567","title":"Adaptive and Approximate Orthogonal Range Counting","year":2016,"lang":"en","type":"article","venue":"ACM Transactions on Algorithms","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Center for Massive Data Algorithmics; Danmarks Grundforskningsfond","keywords":"Range (aeronautics); Mathematics; Combinatorics; Computer science; Discrete mathematics; Algorithm; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002827467,0.000187063,0.0001561542,0.0001674787,0.0002552111,0.0001666267,0.0007869357,0.00005232299,0.00006278072],"category_scores_gemma":[0.00001598454,0.0001334591,0.00006168097,0.000303563,0.00008983236,0.001257652,0.00005639822,0.0001185661,0.0001159607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003202713,"about_ca_system_score_gemma":0.00002043621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001390466,"about_ca_topic_score_gemma":0.000006993845,"domain_scores_codex":[0.9986033,0.00004048217,0.0001921046,0.0005196199,0.0003126159,0.0003318165],"domain_scores_gemma":[0.9988912,0.000169847,0.00006197397,0.0007194054,0.00005243176,0.0001051786],"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":[0.000008820534,0.0000653901,0.00002867208,0.000005745794,0.00003987142,0.0000145052,0.00008653558,0.00001461738,0.00006827123,0.003948994,0.000104434,0.9956142],"study_design_scores_gemma":[0.0120554,0.001738642,0.01314129,0.0006292575,0.0002539737,0.0002679926,0.0006212313,0.847144,0.005443402,0.05913751,0.05588268,0.003684589],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009546555,0.00004809976,0.9955642,0.001992467,0.0003742828,0.0001808568,0.00005458225,0.0002645979,0.0005662595],"genre_scores_gemma":[0.2991363,0.0004202008,0.696058,0.0008559849,0.0001925941,0.0001291129,0.00000548036,0.00004384205,0.003158464],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9919295,"threshold_uncertainty_score":0.5442303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02557180926512948,"score_gpt":0.2399880724752892,"score_spread":0.2144162632101598,"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."}}