{"id":"W4289655394","doi":"10.1109/isit50566.2022.9834716","title":"Calibration with Privacy in Peer Review","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Symposium on Information Theory (ISIT)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Google","keywords":"Computer science; Pareto principle; Adversary; Block (permutation group theory); Calibration; Identity (music); Theoretical computer science; Adversary model; Information retrieval; Data mining; Computer security; Mathematical optimization; Mathematics","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":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.002251672,0.0001930148,0.0001829739,0.0004038992,0.0002131331,0.0002440716,0.01363026,0.00005251147,0.0004993561],"category_scores_gemma":[0.002329592,0.0001796199,0.00005915501,0.0008074426,0.00004733153,0.003988354,0.01142908,0.0005285061,0.0001279009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000582548,"about_ca_system_score_gemma":0.00009738049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001878085,"about_ca_topic_score_gemma":0.000002318998,"domain_scores_codex":[0.9967513,0.0002402712,0.0006117183,0.0003174704,0.001834357,0.0002448646],"domain_scores_gemma":[0.9965076,0.0002030393,0.000385315,0.002611016,0.0002455461,0.00004744517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002203849,0.0002063622,0.000566371,0.0001518455,0.00008030832,0.00003164166,0.001080463,0.007727542,0.0002643503,0.3268655,0.6478869,0.01491825],"study_design_scores_gemma":[0.001876183,0.0005422111,0.0006319575,0.0005229264,0.00001820858,0.0002203975,0.0002847102,0.2731672,0.003348006,0.1850671,0.5334198,0.0009012172],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0107875,0.0002718827,0.4817193,0.441086,0.004419104,0.001924951,0.000454441,0.001596187,0.05774061],"genre_scores_gemma":[0.8630037,0.001789536,0.04528926,0.07691094,0.0003493715,0.00318658,0.004244838,0.00009893004,0.00512686],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8522162,"threshold_uncertainty_score":0.9965663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01755080324033264,"score_gpt":0.2693768819320051,"score_spread":0.2518260786916725,"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."}}