{"id":"W2989796728","doi":"10.4230/lipics.opodis.2019.16","title":"Byzantine-Tolerant Set-Constrained Delivery Broadcast","year":2020,"lang":"en","type":"preprint","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Distributed systems and fault tolerance","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Set (abstract data type); Object (grammar); Mathematics; Discrete mathematics; Combinatorics; Task (project management); Computer science; Artificial intelligence","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":[0.002192068,0.0008922874,0.001594602,0.0006989074,0.001796231,0.002623303,0.002739536,0.001429961,0.007523652],"category_scores_gemma":[0.007831935,0.0005590735,0.001167305,0.001050305,0.001434942,0.005385094,0.005122995,0.002598114,0.001738754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002852527,"about_ca_system_score_gemma":0.002481369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003305935,"about_ca_topic_score_gemma":0.004436563,"domain_scores_codex":[0.9967543,0.000662957,0.0002511261,0.0007971303,0.0009513195,0.000583131],"domain_scores_gemma":[0.9935441,0.002381326,0.0005979974,0.002340875,0.0008537349,0.0002819776],"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":[0.002728398,0.0003348934,0.002318342,0.001560976,0.0002609767,0.0009323661,0.00312837,0.2253003,0.05233164,0.511175,0.03186104,0.1680676],"study_design_scores_gemma":[0.0002648479,0.0003139061,0.0006107368,0.0001119871,0.0001392697,0.0004141656,0.0005464419,0.667518,0.02619248,0.2624303,0.04135388,0.0001039918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1025168,0.0008104509,0.8619615,0.001760503,0.0004027355,0.0005192367,0.001301229,0.00611845,0.02460898],"genre_scores_gemma":[0.784676,0.0004411792,0.184819,0.0009585847,0.0001817377,0.0005364384,0.001690246,0.0006705236,0.02602628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007523652,"threshold_uncertainty_score":0.02516913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03014936134050221,"score_gpt":0.2638968845385422,"score_spread":0.23374752319804,"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."}}