{"id":"W1998371685","doi":"10.1109/mis.2012.26","title":"Recordkeeping and Linking Government Data in Canada","year":2012,"lang":"en","type":"article","venue":"IEEE Intelligent Systems","topic":"Library Science and Information Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Library and Archives Canada","funders":"","keywords":"Computer science; Government (linguistics); E-Government; Data science; Knowledge management; World Wide Web; Information and Communications Technology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001039455,0.0001052935,0.0001636637,0.00004911916,0.00006488086,0.0002675625,0.001248681,0.00002968616,0.000003478959],"category_scores_gemma":[0.00001952099,0.00009139327,0.00001053988,0.0002526297,0.0000117497,0.00491243,0.0003283317,0.00009197352,0.00004377801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003557354,"about_ca_system_score_gemma":0.0002537896,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3881013,"about_ca_topic_score_gemma":0.06720196,"domain_scores_codex":[0.9982197,0.00007200584,0.0005133139,0.000234717,0.0005966295,0.0003636658],"domain_scores_gemma":[0.998816,0.00009761109,0.0001622995,0.000755462,0.00001577512,0.0001528988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005049396,0.00004849172,0.8887378,0.0003298226,0.00003533215,0.00001884315,0.01239619,0.001579693,0.0002885523,0.01046788,0.01018726,0.0759051],"study_design_scores_gemma":[0.0001926375,0.00003215077,0.01012966,0.0005562519,0.000001973557,0.0001448307,0.00732259,0.6869892,0.002709768,0.00003414189,0.2913011,0.000585646],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4893238,0.00341859,0.4710715,0.0006429174,0.02335922,0.0009491665,0.00004708288,0.0001249125,0.01106284],"genre_scores_gemma":[0.9988657,0.00005268754,0.0003700564,0.0003037022,0.00020227,0.000009836262,0.000003248653,0.00000401062,0.0001885241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8786081,"threshold_uncertainty_score":0.9498191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05669753992007824,"score_gpt":0.2406330760244292,"score_spread":0.183935536104351,"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."}}