{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001309333,0.001302241,0.001782991,0.002090402,0.001063854,0.003201284,0.004813815,0.00133406,0.0100853],"category_scores_gemma":[0.0108287,0.0006591307,0.001509757,0.005526169,0.001902931,0.01040689,0.007229673,0.002805294,0.002848474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001615054,"about_ca_system_score_gemma":0.0015719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00194648,"about_ca_topic_score_gemma":0.001808441,"domain_scores_codex":[0.9942478,0.0006213906,0.0004756422,0.001282649,0.002638798,0.0007336882],"domain_scores_gemma":[0.9923409,0.001838525,0.0006529286,0.004003104,0.0009151829,0.0002494497],"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":[0.0008751272,0.0003072404,0.003934962,0.0005569303,0.0001182823,0.0002737943,0.0004405034,0.1212517,0.02271321,0.3655705,0.02419476,0.459763],"study_design_scores_gemma":[0.0001089818,0.000231144,0.001069127,0.00007721576,0.00007800802,0.0006985056,0.000181233,0.6388398,0.01802117,0.2990319,0.04154897,0.000114001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03282984,0.0009976811,0.9460428,0.0005694762,0.0001737187,0.000151436,0.0008977335,0.003093117,0.01524421],"genre_scores_gemma":[0.3597301,0.001010181,0.6234173,0.0007534077,0.0004311012,0.0006316061,0.002837024,0.0006740406,0.01051524],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0100853,"threshold_uncertainty_score":0.03373861,"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."}}