{"id":"W2995071611","doi":"10.5195/ledger.2019.166","title":"Decentralized Common Knowledge Oracles","year":2019,"lang":"en","type":"article","venue":"Ledger","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Glycemic Index Laboratories","funders":"","keywords":"Oracle; Set (abstract data type); Outcome (game theory); Incentive; Common knowledge (logic); Incentive compatibility; A priori and a posteriori","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":[0.007091803,0.0008467654,0.001473113,0.001278601,0.001285119,0.003893852,0.004088728,0.002873398,0.009782407],"category_scores_gemma":[0.02388455,0.0006048318,0.0009301268,0.001450623,0.004116958,0.007767431,0.004991511,0.002301068,0.001104992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002156204,"about_ca_system_score_gemma":0.003192026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001614698,"about_ca_topic_score_gemma":0.00142084,"domain_scores_codex":[0.992574,0.002472475,0.0004108597,0.001478808,0.001929676,0.001134228],"domain_scores_gemma":[0.9823213,0.008524536,0.002224315,0.004259692,0.001597557,0.001072656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002779898,0.0001352639,0.001210834,0.000150262,0.00005325415,0.0003634826,0.0003992304,0.09800489,0.002502033,0.8733577,0.001838199,0.02170704],"study_design_scores_gemma":[0.000175741,0.0001414105,0.0003073043,0.00005347207,0.00002537411,0.000184647,0.0001078617,0.3762743,0.00237633,0.6133704,0.006939722,0.00004347986],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08072103,0.0003495832,0.885591,0.001882632,0.00008118559,0.0006113217,0.000563457,0.0007314491,0.02946838],"genre_scores_gemma":[0.9185339,0.0002259921,0.07083718,0.0002048921,0.00006595148,0.0005242291,0.0002756685,0.00006298413,0.009269176],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009782407,"threshold_uncertainty_score":0.03750551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007929164120913396,"score_gpt":0.2437013506232462,"score_spread":0.2357721865023328,"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."}}