{"id":"W2887000081","doi":"10.1109/lwc.2018.2864758","title":"Incentivizing Consensus Propagation in Proof-of-Stake Based Consortium Blockchain Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Ministry of Education, India; Nanyang Technological University; National Research Foundation of Korea; Energy Market Authority of Singapore; Israel Science Foundation; National Research Foundation Singapore; National Research Foundation","keywords":"Blockchain; Stackelberg competition; Computer science; Database transaction; Proof-of-work system; Block (permutation group theory); Backward induction; Wireless network; Wireless; Cryptocurrency; Uniqueness; Computer network; Game theory; Computer security; Telecommunications; Mathematics; Mathematical economics","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":[0.004224792,0.0009066607,0.001050324,0.000589238,0.0009731255,0.001809872,0.001970691,0.002022712,0.003170452],"category_scores_gemma":[0.01609903,0.0004656334,0.0005994304,0.0006977857,0.001823676,0.004429963,0.002234424,0.001677458,0.000345158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001894104,"about_ca_system_score_gemma":0.0017622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00154676,"about_ca_topic_score_gemma":0.001288144,"domain_scores_codex":[0.9966821,0.001820321,0.0001082553,0.0004591006,0.0004687176,0.0004614526],"domain_scores_gemma":[0.9881285,0.00882105,0.001101851,0.0006812238,0.0006353948,0.0006320304],"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":[0.0006320236,0.0002688954,0.002189615,0.0002586019,0.00009344765,0.0009810749,0.0004798802,0.634034,0.01088237,0.3210945,0.001714979,0.02737058],"study_design_scores_gemma":[0.00005012411,0.000104551,0.0001398593,0.00001196907,0.00001512654,0.00008795168,0.00005416629,0.9306656,0.001099267,0.0669415,0.0008102604,0.00001963495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1882724,0.0004008403,0.8014453,0.0009410756,0.00008904228,0.0002155573,0.0001157637,0.0001899384,0.008329961],"genre_scores_gemma":[0.9834672,0.0001566738,0.01423961,0.00005594548,0.00002600407,0.00009307602,0.00002778756,0.00001727114,0.001916366],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004224792,"threshold_uncertainty_score":0.0223431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02223478288095028,"score_gpt":0.2551769841751901,"score_spread":0.2329422012942398,"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."}}