{"id":"W2079241163","doi":"10.1109/qbsc.2014.6841182","title":"Communication-efficient decentralized quickest change detection","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Fusion center; False alarm; Metric (unit); Computer science; Change detection; Performance metric; Real-time computing; Constraint (computer-aided design); Sensor fusion; Constant false alarm rate; Communications system; Algorithm; Mathematical optimization; Artificial intelligence; Engineering; Mathematics; Telecommunications; Cognitive radio; Wireless","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":[0.001345326,0.00009537608,0.0001604163,0.0001045449,0.0002652332,0.0001323028,0.0005736789,0.00004376828,0.0002804298],"category_scores_gemma":[0.005364801,0.00007031782,0.00003903188,0.0005745584,0.0000982595,0.0002065712,0.0001269698,0.0001140953,0.0007138483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004690646,"about_ca_system_score_gemma":0.000007890746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005512831,"about_ca_topic_score_gemma":0.00009476976,"domain_scores_codex":[0.9980721,0.0001966205,0.0003959771,0.0003023717,0.0007970292,0.0002358847],"domain_scores_gemma":[0.9968061,0.001967205,0.0001348113,0.0006734168,0.0002836651,0.0001347927],"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":[0.00002720547,0.00008180485,0.001671252,0.000004037384,0.000003593936,5.033613e-7,0.0005976783,0.0007657615,0.0006637266,0.03161538,0.0001306395,0.9644384],"study_design_scores_gemma":[0.00158844,0.0001766996,0.09479712,0.00004432791,0.00002134119,0.00001116189,0.001783783,0.4758384,0.01536455,0.2229525,0.1866718,0.000749804],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03911504,0.0001212813,0.9506822,0.0005300009,0.0003767767,0.0001657249,0.00000273936,0.0001009784,0.00890525],"genre_scores_gemma":[0.9822733,0.00001401511,0.01708333,0.0001948835,0.00007674619,0.00003533023,0.000001511958,0.000008926781,0.0003119982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9636886,"threshold_uncertainty_score":0.9175314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1716511458603192,"score_gpt":0.4339494579318372,"score_spread":0.262298312071518,"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."}}