{"id":"W4387565284","doi":"10.20944/preprints202310.0408.v1","title":"Two-way Linear Probing Revisited","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hash function; Hash table; Matching (statistics); Mathematics; Constant (computer programming); Construct (python library); Combinatorics; Cluster (spacecraft); Binary logarithm; Discrete mathematics; Algorithm; Computer science; Statistics","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":[0.002423489,0.0008632524,0.0010769,0.0008875998,0.001121496,0.002587538,0.003529741,0.002177704,0.006995354],"category_scores_gemma":[0.0107532,0.000834347,0.0008556523,0.002473403,0.003352671,0.009204129,0.006159147,0.003531472,0.00240107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001400276,"about_ca_system_score_gemma":0.001210796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005143486,"about_ca_topic_score_gemma":0.0003584676,"domain_scores_codex":[0.9957358,0.001297936,0.000213433,0.0007105439,0.001354365,0.0006879092],"domain_scores_gemma":[0.991468,0.002466415,0.0004958275,0.00453678,0.0007363288,0.0002966829],"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":[0.001152194,0.0002930975,0.002206864,0.0006775837,0.0001047831,0.0003479318,0.0009917109,0.06371089,0.04060943,0.644219,0.01064518,0.2350413],"study_design_scores_gemma":[0.0002054752,0.0005437005,0.000621272,0.00009630532,0.00006212616,0.001402594,0.0002826969,0.5173884,0.05528446,0.3897147,0.03420783,0.0001903697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0243509,0.001008904,0.963941,0.0009206136,0.0001813633,0.0001312736,0.0001468119,0.00193993,0.007379107],"genre_scores_gemma":[0.6318966,0.0008778052,0.3520481,0.0009698111,0.0001929944,0.0004920168,0.0003667665,0.0003817081,0.01277418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006995354,"threshold_uncertainty_score":0.0234018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1665808779111216,"score_gpt":0.366916113659951,"score_spread":0.2003352357488294,"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."}}