{"id":"W2963163286","doi":"10.1109/tcomm.2018.2863377","title":"Analysis and Code Design for the Binary CEO Problem Under Logarithmic Loss","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Low-density parity-check code; Algorithm; Binary number; Generator matrix; Logarithm; Mathematics; Upper and lower bounds; Encoder; Estimator; Decoding methods; Computer science; Arithmetic; Statistics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005551989,0.0001396327,0.0001544949,0.0002311779,0.002316902,0.0001906678,0.0022419,0.0000574181,0.00002605189],"category_scores_gemma":[0.000007472687,0.0001122933,0.0001170798,0.001418473,0.0004746674,0.0002554826,0.00004305354,0.0002611838,0.00002257824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005401406,"about_ca_system_score_gemma":0.00007673167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001595713,"about_ca_topic_score_gemma":0.001158079,"domain_scores_codex":[0.9988064,0.0003629085,0.0002528657,0.0002656833,0.0001183907,0.0001937491],"domain_scores_gemma":[0.994903,0.001744124,0.00008416338,0.002915846,0.0002858815,0.00006700408],"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":[0.0001463606,0.001942004,0.0001575735,0.00002751443,0.003871914,0.000001006562,0.01002015,0.1282944,0.003890568,0.192572,0.004236873,0.6548396],"study_design_scores_gemma":[0.0003840622,0.0001810262,0.0007448345,0.00001903618,0.0002862528,0.000006462109,0.00008997744,0.9801654,0.001294825,0.002095182,0.01452099,0.0002119448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001428386,0.0005209003,0.9808964,0.01725547,0.0000910832,0.0005597899,0.00001978395,0.0001425197,0.0003712209],"genre_scores_gemma":[0.8646034,0.003287903,0.1305676,0.000655437,0.00001684134,0.000380924,0.000003838135,0.00001095871,0.0004731015],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8644605,"threshold_uncertainty_score":0.998982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1009782085967124,"score_gpt":0.3284064398404089,"score_spread":0.2274282312436965,"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."}}