{"id":"W2966205006","doi":"10.1145/3595180","title":"Competitive Online Search Trees on Trees","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Algorithms","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission; Fonds De La Recherche Scientifique - FNRS; York University; National Science Foundation","keywords":"Ternary search tree; Weight-balanced tree; Binary search tree; Optimal binary search tree; Range tree; Self-balancing binary search tree; K-ary tree; Mathematics; Binary tree; Tree (set theory); Combinatorics; Generalization; Vertex (graph theory); Search tree; Path (computing); Interval tree; Set (abstract data type); Search algorithm; Computer science; Tree structure; Algorithm; Graph","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":[0.001817383,0.0006193981,0.001634716,0.0009599797,0.0007029756,0.002105865,0.002364709,0.001261219,0.004331308],"category_scores_gemma":[0.01275304,0.0005474,0.0006933956,0.002943854,0.001099952,0.005805254,0.002060421,0.001367321,0.001259072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001313038,"about_ca_system_score_gemma":0.001391521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001776512,"about_ca_topic_score_gemma":0.002002433,"domain_scores_codex":[0.9976526,0.0007939545,0.0001469844,0.0003892016,0.0007002763,0.0003170757],"domain_scores_gemma":[0.994968,0.002843884,0.0004105822,0.0009299343,0.0005196282,0.0003279168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005154173,0.0003090349,0.001931387,0.0003957596,0.00007961198,0.0001802315,0.0003255176,0.3908382,0.005038557,0.4140162,0.01309879,0.1732713],"study_design_scores_gemma":[0.00006162872,0.0001642328,0.0001582559,0.00001628328,0.00001583977,0.0001220668,0.00004031501,0.8424259,0.001169907,0.1507037,0.005107454,0.00001434077],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04979324,0.0008942786,0.9412731,0.0005319376,0.00006929532,0.0001403311,0.0004573897,0.0006889991,0.006151457],"genre_scores_gemma":[0.5171866,0.001031205,0.4721452,0.0003958805,0.0001709628,0.0004279243,0.0009579753,0.0002610068,0.007423222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004331308,"threshold_uncertainty_score":0.01448971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04541435443245161,"score_gpt":0.3085284622071648,"score_spread":0.2631141077747132,"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."}}