{"id":"W1993660618","doi":"10.1109/isi.2011.5984777","title":"Enabling dynamic linkage of linguistic census data at Statistics Canada (extended abstract)","year":2011,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Census; Computer science; Population; Data science; USable; Sample (material); Dataflow; Geography; Sociology; World Wide Web; Demography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005754839,0.0005403506,0.0006597976,0.001589401,0.002759084,0.005138492,0.002270796,0.0008858716,0.006306429],"category_scores_gemma":[0.03747738,0.0004384283,0.0007074226,0.006060744,0.001629731,0.002888396,0.006073171,0.00142417,0.001371527],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01527547,"about_ca_system_score_gemma":0.03271455,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.767567,"about_ca_topic_score_gemma":0.7259946,"domain_scores_codex":[0.9931889,0.001060748,0.0003776183,0.001186422,0.003219231,0.0009671925],"domain_scores_gemma":[0.9830642,0.005088795,0.0009685447,0.005115916,0.004669797,0.001092631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002459958,0.0003290218,0.1307959,0.0005466297,0.0003025034,0.001637658,0.007906848,0.1959742,0.01060852,0.1676102,0.1969865,0.2848421],"study_design_scores_gemma":[0.0001770975,0.00008789899,0.03028118,0.0002131005,0.000063978,0.0003040845,0.00245474,0.6281853,0.02232965,0.09664924,0.218988,0.0002657509],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.24133,0.0009815221,0.4884061,0.02142522,0.0005992373,0.001429862,0.1615428,0.0408032,0.04348215],"genre_scores_gemma":[0.8011329,0.0005028772,0.15163,0.001363736,0.000116145,0.0004432065,0.03584983,0.001005806,0.007955498],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9847245,"threshold_uncertainty_score":0.4676037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06611871046369332,"score_gpt":0.2837272785525858,"score_spread":0.2176085680888925,"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."}}