{"id":"W4245730866","doi":"10.1137/1.9781611973105.18","title":"Adaptive and Approximate Orthogonal Range Counting","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Center for Massive Data Algorithmics","keywords":"Range (aeronautics); Computer science; Algorithm; Mathematics; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001198453,0.00005873275,0.00005602415,0.00003394673,0.00005885054,0.0003175369,0.0002764649,0.00001146822,0.00008762904],"category_scores_gemma":[0.000004082849,0.0000454312,0.0000109147,0.0000989769,0.00002104583,0.001317707,0.0003610425,0.00003359793,0.0001648161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003254434,"about_ca_system_score_gemma":0.000003957686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004802972,"about_ca_topic_score_gemma":0.000003251859,"domain_scores_codex":[0.9994776,0.000008942762,0.00006954399,0.000188758,0.0001125856,0.0001425921],"domain_scores_gemma":[0.9997297,0.00001895902,0.00002303074,0.0001657435,0.0000261268,0.00003642973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001040075,0.00002946123,0.002876753,0.0000141799,0.00002125565,0.000007431885,0.0001944926,0.000003422818,0.00005171062,0.6300528,0.01265861,0.3540888],"study_design_scores_gemma":[0.0002796104,0.00002994414,0.01351654,0.000006997428,0.00000309826,0.000004201825,0.00009440709,0.9716326,0.00005095855,0.009756989,0.00445047,0.0001742074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0100234,0.00004257173,0.9408886,0.0006796435,0.00009702773,0.0001711662,0.000001238569,0.0001556274,0.04794067],"genre_scores_gemma":[0.6155651,0.00002442951,0.3768604,0.001264928,0.00009874751,0.00004229152,0.000005802965,0.000008361299,0.006129899],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9716291,"threshold_uncertainty_score":0.3062015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01567146947089936,"score_gpt":0.2049197532304933,"score_spread":0.189248283759594,"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."}}