{"id":"W2131197449","doi":"10.1142/s0218195905001853","title":"ON MULTI-LEVEL k-RANGES FOR RANGE SEARCH","year":2005,"lang":"en","type":"article","venue":"International Journal of Computational Geometry & Applications","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Range (aeronautics); Mathematics; Combinatorics; Binary logarithm; Tree (set theory); Materials science","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":[0.0005390725,0.0001340767,0.0001583836,0.000747835,0.0001226815,0.0002776809,0.001864853,0.0000391596,0.0000423061],"category_scores_gemma":[0.00004836013,0.0001270956,0.0001646805,0.0004196393,0.00004449441,0.0008041879,0.0001659043,0.0001609457,0.0001259171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001190612,"about_ca_system_score_gemma":0.0001005844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001579423,"about_ca_topic_score_gemma":0.000001253782,"domain_scores_codex":[0.998136,0.00002868221,0.0004919012,0.0002458782,0.0009184197,0.0001791466],"domain_scores_gemma":[0.9975891,0.000553778,0.0002963786,0.0002068498,0.001248704,0.0001052534],"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":[0.00004030117,0.0006268938,0.0001006794,0.00001116107,0.0002320393,0.000003853389,0.00008224202,0.07932038,0.00004106437,0.2707656,0.006064808,0.642711],"study_design_scores_gemma":[0.005690303,0.0003378201,0.01191924,0.00008045663,0.00005067983,0.0001091906,0.00006919908,0.3271835,0.0007386674,0.0811014,0.5721415,0.0005780476],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001042495,0.0001457969,0.9906014,0.006935548,0.0003078955,0.0003749825,0.0001452507,0.00003714924,0.0004095491],"genre_scores_gemma":[0.24264,0.00004177473,0.7536202,0.001366243,0.001437717,0.0001175779,0.0001049942,0.00001532821,0.0006561021],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6421329,"threshold_uncertainty_score":0.5182807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05640688424490003,"score_gpt":0.350634776905452,"score_spread":0.2942278926605519,"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."}}