{"id":"W4293568415","doi":"10.1007/978-3-642-27848-8_631-1","title":"Orthogonal Range Searching on Discrete Grids","year":2014,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Algorithms","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Range (aeronautics); Computer science; Computational science; Engineering; Aerospace engineering","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":[0.0001781102,0.000315399,0.0006211885,0.0005912601,0.0002273967,0.0007169716,0.0006822366,0.0003171037,0.007203684],"category_scores_gemma":[0.001204315,0.0002370591,0.0002033758,0.001590671,0.0005291107,0.001143441,0.001512207,0.0006943938,0.001581229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002876159,"about_ca_system_score_gemma":0.0003090544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006229865,"about_ca_topic_score_gemma":0.0005834461,"domain_scores_codex":[0.9997432,0.00004593375,0.00001456866,0.0000324683,0.0001381838,0.00002572209],"domain_scores_gemma":[0.9997082,0.0001421476,0.00001784027,0.00007357752,0.00004214871,0.00001593385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001633061,0.00005044476,0.0003117924,0.0003303303,0.00001899008,0.00009422516,0.0001228359,0.1096915,0.01087601,0.2934659,0.01904649,0.5658281],"study_design_scores_gemma":[0.00005355929,0.00005934538,0.0002358221,0.00006105916,0.0000097804,0.0003270876,0.00007948168,0.7360143,0.00682302,0.2192889,0.0370229,0.00002485238],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02378719,0.002565777,0.922546,0.0003224983,0.0002469061,0.0000439906,0.0001833033,0.0008809615,0.04942339],"genre_scores_gemma":[0.3102945,0.004515061,0.6460999,0.000201797,0.000211603,0.0001445441,0.0006898046,0.0004940781,0.03734865],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007203684,"threshold_uncertainty_score":0.02409875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01382886315599423,"score_gpt":0.2463296812034269,"score_spread":0.2325008180474327,"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."}}