{"id":"W3009196551","doi":"10.4018/ijssci.2020010104","title":"An Incentive Compatible Mechanism for Replica Placement in Peer-Assisted Content Distribution","year":2020,"lang":"en","type":"article","venue":"International Journal of Software Science and Computational Intelligence","topic":"Peer-to-Peer Network Technologies","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Replica; Computer science; Content delivery; Incentive; Distributed computing; Content distribution; Latency (audio); Key (lock); Incentive compatibility; The Internet; Peer-to-peer; Content delivery network; Computer network; World Wide Web; Computer security; Telecommunications; Server","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":[],"consensus_categories":[],"category_scores_codex":[0.001088095,0.0001434584,0.0002067029,0.000310562,0.0001378563,0.0003965209,0.002271428,0.00004368868,0.000003645256],"category_scores_gemma":[0.001810543,0.0001336771,0.00005348839,0.0008949998,0.0002094433,0.001242081,0.0003664656,0.0001822077,0.000004634147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003330616,"about_ca_system_score_gemma":0.0004184936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001794785,"about_ca_topic_score_gemma":0.000007758978,"domain_scores_codex":[0.9969814,0.00003242768,0.0006230153,0.0004458506,0.00166431,0.0002529354],"domain_scores_gemma":[0.9934745,0.0002643951,0.0003536067,0.0001481976,0.005526998,0.0002323034],"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":[0.0004104465,0.0005206454,0.003784928,0.00002077398,0.00008451541,0.0000958632,0.003477638,0.2018462,0.004299134,0.5018498,0.001664221,0.2819458],"study_design_scores_gemma":[0.000776161,0.001898816,0.01474517,0.0001996578,0.00001062015,0.0001668127,0.001288794,0.822155,0.02251054,0.134696,0.001114295,0.0004381184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06400156,0.0000282549,0.9212486,0.0139526,0.0004360677,0.000225855,0.00003732317,0.00005983208,0.000009941165],"genre_scores_gemma":[0.7975771,0.000009803231,0.2007179,0.001600363,0.00006080321,0.00001333593,0.00001213376,0.000004018485,0.000004488676],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7335756,"threshold_uncertainty_score":0.5451195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06765365080020397,"score_gpt":0.3309161150700873,"score_spread":0.2632624642698833,"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."}}