{"id":"W1984054760","doi":"10.1007/s00236-004-0157-8","title":"A deterministic skip list for k-dimensional range search","year":2005,"lang":"en","type":"article","venue":"Acta Informatica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick; Nova Scotia Community College","funders":"","keywords":"Range (aeronautics); Data structure; Commutative property; Mathematics; Set (abstract data type); Theory of computation; Discrete mathematics; Combinatorics; Cartesian product; Extension (predicate logic); Associative property; Computer science; Algorithm; Pure mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003310551,0.0001051084,0.0001151762,0.0001032314,0.0001469015,0.000325624,0.0008995492,0.00002936136,0.0000614528],"category_scores_gemma":[0.00005692045,0.00008949316,0.00005435193,0.0001562538,0.00003452283,0.002028773,0.0004119409,0.00007115377,0.0003906628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002371729,"about_ca_system_score_gemma":0.00003861076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003201596,"about_ca_topic_score_gemma":0.000002894811,"domain_scores_codex":[0.9990036,0.000009999969,0.0002585514,0.0001336391,0.0002791036,0.0003151084],"domain_scores_gemma":[0.9991957,0.0001477498,0.00005216569,0.0004609742,0.00005306791,0.00009032492],"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":[0.00002750299,0.0001621805,0.00005989338,0.0002257763,0.00006066952,0.000006668141,0.001847199,0.0001251507,0.00004164601,0.08853278,0.186649,0.7222615],"study_design_scores_gemma":[0.0005131248,0.00007655806,0.0002431886,0.00001618129,0.00000703854,0.00000583745,0.00001992996,0.7006685,0.0001094024,0.000339453,0.2978472,0.0001535841],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01392938,0.00003223083,0.9183105,0.01135022,0.0006498395,0.001363907,0.00008533199,0.0004959948,0.05378261],"genre_scores_gemma":[0.6267565,0.00000573138,0.3671941,0.003485905,0.000280281,0.00007569116,0.0000654791,0.00001067145,0.002125692],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7221079,"threshold_uncertainty_score":0.502131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02194112664209232,"score_gpt":0.2681676960358217,"score_spread":0.2462265693937294,"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."}}