{"id":"W4413755894","doi":"10.14778/3718057.3718071","title":"Cabinet: Dynamically Weighted Consensus Made Fast","year":2025,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Distributed systems and fault tolerance","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Concordia University","funders":"","keywords":"Cabinet (room); Consensus conference; Computer science; History; Library science; Archaeology","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":[0.002115306,0.0008429664,0.001025318,0.001040335,0.001425041,0.001245828,0.003060209,0.001182919,0.004163448],"category_scores_gemma":[0.005034852,0.0005221191,0.0004887401,0.00108343,0.0009226926,0.002272297,0.002545103,0.001673021,0.001303215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008459431,"about_ca_system_score_gemma":0.002169573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004265429,"about_ca_topic_score_gemma":0.004625631,"domain_scores_codex":[0.9983,0.0004014107,0.00008588541,0.0003596214,0.0005954641,0.0002575996],"domain_scores_gemma":[0.9971611,0.0007723808,0.0002408105,0.0007943797,0.0007977238,0.0002335628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001601014,0.000270973,0.001611827,0.0004788527,0.0001886666,0.0004000578,0.0005654362,0.4751628,0.04359476,0.04877604,0.04006705,0.3872825],"study_design_scores_gemma":[0.0002030631,0.0002349455,0.0002243866,0.0000200671,0.00002505474,0.0001355602,0.00008576727,0.9560271,0.0128337,0.0165834,0.01358357,0.00004340563],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03144537,0.0004970291,0.9461818,0.000414174,0.0002923459,0.0002838714,0.000275552,0.01459626,0.006013528],"genre_scores_gemma":[0.5295385,0.0002875207,0.4582463,0.0003316316,0.00009563276,0.0005437713,0.001112888,0.0007794704,0.009064437],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004265429,"threshold_uncertainty_score":0.01392812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004561944860713993,"score_gpt":0.2119563332243407,"score_spread":0.2073943883636268,"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."}}