{"id":"W2055270878","doi":"10.48550/arxiv.0908.3234","title":"Overlapped Chunked Network Coding","year":2009,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Linear network coding; Decoding methods; Computer science; Encoding (memory); Coding (social sciences); Computational complexity theory; Context (archaeology); Resilience (materials science); Theoretical computer science; Algorithm; Mathematics; Computer network; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007135922,0.0003916969,0.0004682255,0.0001011068,0.0005119319,0.0003339997,0.003155492,0.0002629905,0.0000665347],"category_scores_gemma":[0.00008282859,0.0004046049,0.0002069908,0.0005344136,0.00005532992,0.0002554131,0.003947719,0.001122354,0.000247543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001365379,"about_ca_system_score_gemma":0.0002059655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004414496,"about_ca_topic_score_gemma":0.00003026346,"domain_scores_codex":[0.9975019,0.0003201638,0.0005326855,0.0008043548,0.000290601,0.0005503377],"domain_scores_gemma":[0.9966499,0.0001606127,0.0003232162,0.002517878,0.0001844561,0.0001639297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005679223,0.0005662213,0.1720904,0.0001943956,0.0005209646,0.0001052279,0.006100448,0.02013695,0.004471156,0.2701984,0.07169387,0.4538652],"study_design_scores_gemma":[0.001289495,0.0001460194,0.5835516,0.00166903,0.00007361425,0.00001968393,0.0000349793,0.262655,0.0008536059,0.01495755,0.1320144,0.002734934],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2547625,0.005889238,0.699308,0.006321792,0.003357038,0.0008683288,0.000003585275,0.001440983,0.02804855],"genre_scores_gemma":[0.9689474,0.002854205,0.02455459,0.002077567,0.0007793441,0.00004137403,0.00003175449,0.00002558164,0.0006881918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7141849,"threshold_uncertainty_score":0.9998406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08308879224724217,"score_gpt":0.3011594798972222,"score_spread":0.21807068764998,"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."}}