{"id":"W2070295459","doi":"10.3390/e17031379","title":"The Optimal Fix-Free Code for Anti-Uniform Sources","year":2015,"lang":"en","type":"article","venue":"Entropy","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Huffman coding; Code (set theory); Canonical Huffman code; Source code; Symbol (formal); Class (philosophy); Prefix code; Universal code; Computer science; Mathematics; Combinatorics; Programming language; Algorithm; Code rate; Linear code; Decoding methods; Systematic code; Block code","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":[0.000780935,0.0004707111,0.0004771399,0.001026383,0.0004712063,0.0007063706,0.0005359435,0.0006353517,0.00133808],"category_scores_gemma":[0.004293087,0.0002554929,0.000271529,0.0007397569,0.001073433,0.001439241,0.001233177,0.0007841577,0.0003174933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007710782,"about_ca_system_score_gemma":0.001043866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001020527,"about_ca_topic_score_gemma":0.000861393,"domain_scores_codex":[0.9992856,0.0001658733,0.00003777302,0.0001205334,0.0002918206,0.00009840744],"domain_scores_gemma":[0.998145,0.0009342458,0.000214364,0.0002834856,0.0003384463,0.00008448281],"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":[0.0006924497,0.00004495197,0.001335726,0.00015458,0.00005693338,0.0002682965,0.0002107006,0.1793958,0.02929638,0.6777437,0.003619845,0.1071807],"study_design_scores_gemma":[0.00004732743,0.00007956009,0.000557626,0.00004689817,0.00002460967,0.0003445102,0.00004966366,0.7505366,0.02637513,0.2183744,0.003503278,0.00006052059],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.161635,0.0007971661,0.8282236,0.0005765169,0.00008032234,0.00003509478,0.0002190882,0.0002890517,0.008144085],"genre_scores_gemma":[0.8455862,0.0005983233,0.1470616,0.0002752348,0.000116689,0.0000926142,0.0004306065,0.0001419863,0.005696735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00133808,"threshold_uncertainty_score":0.005594552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02572240566034025,"score_gpt":0.262229856858203,"score_spread":0.2365074511978627,"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."}}