{"id":"W2051671786","doi":"10.1007/s00453-012-9664-0","title":"A Uniform Paradigm to Succinctly Encode Various Families of Trees","year":2012,"lang":"en","type":"article","venue":"Algorithmica","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"ENCODE; Computer science; Theory of computation; Combinatorics; Weight-balanced tree; Theoretical computer science; Mathematics; Set (abstract data type); Encoding (memory); Tree (set theory); Node (physics); Discrete mathematics; Algorithm; Binary tree; Artificial intelligence; Binary search tree","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":[0.0002493348,0.000188073,0.0002694496,0.0001711252,0.0001099186,0.00005638911,0.00106545,0.00007427423,0.00002371229],"category_scores_gemma":[0.00002927865,0.0001545851,0.00007561668,0.0004856727,0.00004279045,0.0008377074,0.0006086222,0.0001155885,0.0001298163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003818246,"about_ca_system_score_gemma":0.00005776579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004276433,"about_ca_topic_score_gemma":0.00003330113,"domain_scores_codex":[0.9984761,0.00004183144,0.0003149056,0.0003076961,0.0003558483,0.0005035679],"domain_scores_gemma":[0.9985918,0.0001131462,0.0001025825,0.0008594177,0.00005409744,0.0002789855],"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":[0.0000288238,0.000676073,0.003854777,0.00003676081,0.00008722745,0.00002267239,0.005819486,0.0001515427,0.002886508,0.1270986,0.01227252,0.847065],"study_design_scores_gemma":[0.002122913,0.001259617,0.09634861,0.0002329163,0.00008138841,0.0002038856,0.0004868812,0.1902912,0.05948371,0.01811548,0.629258,0.002115464],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02058113,0.0003144569,0.9745464,0.0006644122,0.0006986069,0.0002267151,0.00003804389,0.0001727292,0.002757558],"genre_scores_gemma":[0.6653494,0.00003329524,0.3337331,0.0004025417,0.0002389996,0.00002169835,0.00001144561,0.00001529796,0.0001942899],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8449496,"threshold_uncertainty_score":0.6303796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01263315840486076,"score_gpt":0.2454722923001212,"score_spread":0.2328391338952605,"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."}}