{"id":"W4206345476","doi":"10.1109/bigdata52589.2021.9671778","title":"An Implementation of Fake News Prevention by Blockchain and Entropy-based Incentive Mechanism","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Big Data (Big Data)","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Computer security; Incentive; Internet privacy; Fake news; Crash; Deception; Social media; Mechanism (biology); Cheating; World Wide Web","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":[0.0004267025,0.0001819689,0.0002042425,0.000156078,0.0001135348,0.0001968671,0.003116736,0.0001174703,0.0001314237],"category_scores_gemma":[0.00005521481,0.0001965514,0.00002507734,0.0002912707,0.00009285701,0.0004463209,0.001112806,0.0002045346,0.00001233239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004271689,"about_ca_system_score_gemma":0.0003611545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000284802,"about_ca_topic_score_gemma":0.001382131,"domain_scores_codex":[0.9976719,0.0001571588,0.0004155531,0.001077696,0.0004769402,0.0002007757],"domain_scores_gemma":[0.9966587,0.00006390792,0.0003050793,0.002541631,0.0003451259,0.00008556384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000215568,0.0005834379,0.0004466222,0.00001663919,0.0001108937,0.0000139832,0.0001167465,0.000005686174,0.05659449,0.3524899,0.003917685,0.5856823],"study_design_scores_gemma":[0.003081027,0.0004646424,0.001037017,0.000167826,0.00008540202,0.0000262624,0.001411366,0.6092454,0.2861598,0.07417575,0.02333526,0.0008103571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07943682,0.00006043079,0.9054919,0.005073314,0.0009263349,0.000320957,0.008475164,0.00006885211,0.0001461974],"genre_scores_gemma":[0.9723108,0.0002118642,0.01299956,0.000293471,0.0001346163,0.00003601659,0.0139545,0.000009216359,0.00004997153],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8928739,"threshold_uncertainty_score":0.8015132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1345314604793738,"score_gpt":0.3585768073854467,"score_spread":0.2240453469060729,"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."}}