{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09508976,0.001033312,0.001579753,0.00634,0.006150823,0.01726128,0.00349774,0.0117388,0.0365558],"category_scores_gemma":[0.2399267,0.001982932,0.001466728,0.0155964,0.00982536,0.01310694,0.007599523,0.01195608,0.02852676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007443897,"about_ca_system_score_gemma":0.04107604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02207294,"about_ca_topic_score_gemma":0.02106174,"domain_scores_codex":[0.9088143,0.04758705,0.008163839,0.004612129,0.02723744,0.003585235],"domain_scores_gemma":[0.7543278,0.1644419,0.009103312,0.03879423,0.03113159,0.002201175],"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":[0.00002872958,0.00001783204,0.0004513944,0.0002091953,0.00001791952,0.00006144445,0.00101472,0.0002020342,0.00007026753,0.3998401,0.558094,0.0399924],"study_design_scores_gemma":[0.00001902474,0.0000165897,0.0009491745,0.001331667,0.0000302757,0.0001102826,0.0005322801,0.0004654811,0.0003238858,0.1545006,0.8416762,0.00004455289],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001887127,0.01116258,0.04853036,0.2396641,0.007233519,0.0007074993,0.01023151,0.001643905,0.6789395],"genre_scores_gemma":[0.1009051,0.03516496,0.09640516,0.1982959,0.01287317,0.006611304,0.02288222,0.003600973,0.5232612],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.09508976,"threshold_uncertainty_score":0.5028887,"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."}}