{"id":"W2065060855","doi":"10.1109/tit.2013.2295392","title":"A Universal Grammar-Based Code for Lossless Compression of Binary Trees","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Code word; Lossless compression; Binary tree; Computer science; Theoretical computer science; Random binary tree; K-ary tree; Binary number; Binary code; Mathematics; Algorithm; Decoding methods; Data compression; Tree structure; Arithmetic","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":[0.0003888402,0.0001243415,0.000149892,0.0002622992,0.0002328954,0.0000572944,0.0004436551,0.0000768946,0.00002380361],"category_scores_gemma":[0.000008329645,0.0001064815,0.0001031594,0.0002035353,0.00006922818,0.001532446,0.000004556414,0.0001038701,0.00002094894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003028876,"about_ca_system_score_gemma":0.00005481033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008032273,"about_ca_topic_score_gemma":0.000002854242,"domain_scores_codex":[0.9990865,0.0001001046,0.0003077531,0.0001289285,0.0002251761,0.0001515252],"domain_scores_gemma":[0.9987678,0.0003948898,0.0001819135,0.0004496657,0.0001382825,0.00006747643],"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":[0.0007331982,0.0003409655,0.000004153707,0.0001500275,0.00003726474,3.987471e-7,0.001180329,0.2397578,0.002138881,0.1033313,0.0009450083,0.6513807],"study_design_scores_gemma":[0.001601054,0.0004150022,0.00008796972,0.00009571778,0.00001533958,0.000001719769,0.00011086,0.9527835,0.03074013,0.003605494,0.01037022,0.0001730098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002592736,0.00000474412,0.9958956,0.0001019814,0.0004764377,0.0002458238,0.0001642926,0.0001392116,0.0003792042],"genre_scores_gemma":[0.965326,0.000003791024,0.03427626,0.0002342822,0.00001625182,0.00003650814,0.00003488136,0.00000643569,0.00006559391],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9627333,"threshold_uncertainty_score":0.4342192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01103379855174834,"score_gpt":0.2315401169764841,"score_spread":0.2205063184247357,"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."}}