{"id":"W4300527060","doi":"10.48550/arxiv.1209.0491","title":"Coding Opportunity Densification Strategies for Instantly Decodable\\n Network Coding","year":2012,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Goodput; Linear network coding; Network packet; Erasure; Computer science; Coding (social sciences); Monotonic function; Backhaul (telecommunications); Theoretical computer science; Maximization; Mathematical optimization; Algorithm; Computer network; Mathematics; Throughput; Telecommunications; Base station; Wireless","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":[0.001153652,0.0007533435,0.0004345963,0.000658175,0.0004677322,0.0008049706,0.00112326,0.0005460961,0.001315495],"category_scores_gemma":[0.005815026,0.0003181703,0.0002078546,0.0005412311,0.001562677,0.00166021,0.001626806,0.0009360044,0.000150996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001333314,"about_ca_system_score_gemma":0.001050887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001733607,"about_ca_topic_score_gemma":0.002746548,"domain_scores_codex":[0.9993581,0.000203724,0.00001978088,0.00009590066,0.0001865902,0.0001359181],"domain_scores_gemma":[0.9965635,0.00245443,0.0003256726,0.0002462874,0.0002738251,0.000136392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002206252,0.00009718378,0.0007809118,0.0001325255,0.00003543791,0.0002560632,0.0003455046,0.7370881,0.02048156,0.1853382,0.001040505,0.05418339],"study_design_scores_gemma":[0.00001922064,0.00008829805,0.0002434205,0.00002339967,0.00001500084,0.0001253714,0.00007497533,0.9566438,0.006825352,0.03463917,0.001278592,0.0000233781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0951611,0.0005579964,0.897114,0.0002818529,0.00004452477,0.00008247419,0.00004706315,0.0001991219,0.006511951],"genre_scores_gemma":[0.9349207,0.0002775184,0.06216398,0.00006622609,0.00002290099,0.00006734558,0.00002427071,0.0000303416,0.00242675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001733607,"threshold_uncertainty_score":0.009673893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2549399284525203,"score_gpt":0.2520638010510898,"score_spread":0.002876127401430506,"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."}}