{"id":"W2762265243","doi":"10.17226/24893","title":"Federal Statistics, Multiple Data Sources, and Privacy Protection","year":2017,"lang":"en","type":"book","venue":"National Academies Press eBooks","topic":"Census and Population Estimation","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Agricultural Statistics Service; York University; Laura and John Arnold Foundation; National Science Foundation","keywords":"Internet privacy; Privacy protection; Computer science; Statistics; Computer security; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005971338,0.0003267833,0.0003667003,0.0001139842,0.0005715645,0.0002225421,0.0005990858,0.0006697173,0.00002990691],"category_scores_gemma":[0.002721097,0.0003246103,0.0000389692,0.000004633495,0.0001713354,0.0002651614,0.0005758362,0.0008438554,0.00001120187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000173382,"about_ca_system_score_gemma":0.0002811125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006978738,"about_ca_topic_score_gemma":0.00004863355,"domain_scores_codex":[0.9977454,0.00006459508,0.0005532004,0.0005355344,0.0008933253,0.0002079273],"domain_scores_gemma":[0.9974855,0.000574149,0.0009144811,0.0005531523,0.0003847824,0.00008791355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007201148,0.0000332345,0.0001617143,0.001067508,0.0002008952,0.00000303517,0.0005382924,0.00001894583,0.00001608414,0.558064,0.4074229,0.03240132],"study_design_scores_gemma":[0.0004382207,0.0000146165,0.0005237192,0.0001922276,0.0000650311,0.00001642197,0.000001481366,0.009391167,0.00002371515,0.3422816,0.6467474,0.0003044487],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0004378554,0.001212438,0.04032585,0.0005686856,0.001000218,0.00482295,0.0155364,0.0005869428,0.9355087],"genre_scores_gemma":[0.003626074,0.00006990495,0.01797398,0.00009130828,0.001427939,0.0001098122,0.003710989,0.0000995356,0.9728904],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2393245,"threshold_uncertainty_score":0.9999206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2676436451205496,"score_gpt":0.3866330388798279,"score_spread":0.1189893937592784,"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."}}