{"id":"W4281621365","doi":"10.1145/3519270.3538418","title":"When is Recoverable Consensus Harder Than Consensus?","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Distributed systems and fault tolerance","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Hellenic Foundation for Research and Innovation; Agence Nationale de la Recherche","keywords":"Consensus; Computer science; Uniform consensus; Object (grammar); Class (philosophy); Focus (optics); Consensus algorithm; Consensus conference; Crash; Distributed computing; Multi-agent system; Artificial intelligence; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006968961,0.0007833347,0.002616944,0.001168962,0.002087807,0.00427794,0.002888882,0.006795108,0.00835261],"category_scores_gemma":[0.08037375,0.001105907,0.001587928,0.001925144,0.005028304,0.01829898,0.004114403,0.005554861,0.000868842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001218359,"about_ca_system_score_gemma":0.001029156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001483136,"about_ca_topic_score_gemma":0.0007784056,"domain_scores_codex":[0.9906737,0.002866121,0.0005478346,0.002182167,0.001723694,0.002006348],"domain_scores_gemma":[0.8856031,0.09326133,0.006972091,0.008094488,0.003243468,0.002825466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002996854,0.000996186,0.01708009,0.002231056,0.0008622238,0.002156562,0.00374007,0.4316326,0.01439318,0.3575214,0.02644578,0.139944],"study_design_scores_gemma":[0.0003101458,0.0002471672,0.001989066,0.00008600746,0.00008460236,0.0008229951,0.001728395,0.3110516,0.004852843,0.6747934,0.00394097,0.00009277797],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6225644,0.002686275,0.3275386,0.0277712,0.0005967325,0.0002591877,0.0005460588,0.001102617,0.01693484],"genre_scores_gemma":[0.958762,0.0008678141,0.03478227,0.0009289834,0.0006868377,0.0002171444,0.0003704639,0.0002848061,0.003099606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00835261,"threshold_uncertainty_score":0.03685582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03078858875991975,"score_gpt":0.2649005089795085,"score_spread":0.2341119202195888,"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."}}