{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006005275,0.0003763147,0.0005349954,0.0008728415,0.0004176606,0.000757835,0.0009364831,0.001010199,0.0009893912],"category_scores_gemma":[0.004397377,0.0002103278,0.000361224,0.001132929,0.001382252,0.001395694,0.00133655,0.001087145,0.0003234796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007316735,"about_ca_system_score_gemma":0.00117391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001286465,"about_ca_topic_score_gemma":0.001127524,"domain_scores_codex":[0.9993004,0.0001237832,0.00003821935,0.0001226041,0.0003468512,0.00006808788],"domain_scores_gemma":[0.9983901,0.0007683639,0.0001429522,0.0003498899,0.0002845738,0.00006423194],"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":[0.0002119918,0.00006931799,0.0007352476,0.0003388393,0.000040836,0.0006335202,0.0003935964,0.2174138,0.04006066,0.5287042,0.004217863,0.2071801],"study_design_scores_gemma":[0.00002759446,0.00007937817,0.0001956773,0.00005578734,0.00002370044,0.0005418935,0.00003097524,0.8337594,0.01824654,0.1408184,0.006189205,0.00003151444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03015569,0.0006889895,0.9657736,0.0003182689,0.00006738264,0.00005603114,0.000165129,0.0004799063,0.002295031],"genre_scores_gemma":[0.5684643,0.0013076,0.4237118,0.0004878487,0.0001904074,0.0002675025,0.0006104665,0.0003365515,0.004623508],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001286465,"threshold_uncertainty_score":0.005308688,"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."}}