{"id":"W2618989172","doi":"10.1002/cjs.11322","title":"Dynamic data science and official statistics","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Data science; Statistical inference; Inference; Data quality; Computer science; Variety (cybernetics); Sampling frame; Official statistics; Visualization; Population; Data mining; Econometrics; Statistics; Artificial intelligence; Mathematics; Engineering; Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.05859648,0.0005907248,0.002190943,0.007218419,0.0028965,0.0110821,0.002667254,0.002009216,0.007094258],"category_scores_gemma":[0.2536372,0.0007819411,0.0007965788,0.02144725,0.01635169,0.008209429,0.004503371,0.007562948,0.001101249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01813402,"about_ca_system_score_gemma":0.03393412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1552698,"about_ca_topic_score_gemma":0.08266135,"domain_scores_codex":[0.9262954,0.0439042,0.004072313,0.007463658,0.01658234,0.001682169],"domain_scores_gemma":[0.6929647,0.2004299,0.02257395,0.03788174,0.0414145,0.004735138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002259102,0.00001074859,0.003392787,0.0001605953,0.00004262216,0.00005119714,0.0005591547,0.003617097,0.00002923787,0.9323593,0.02709326,0.03266139],"study_design_scores_gemma":[0.00001732335,0.00001787883,0.002089065,0.0004231865,0.00002217559,0.00008305826,0.0006595131,0.01956895,0.0001202565,0.833073,0.1438779,0.00004768921],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01547476,0.01940959,0.7414793,0.1265494,0.003474789,0.0003037168,0.01018062,0.001389105,0.08173866],"genre_scores_gemma":[0.6770115,0.02143204,0.2586509,0.01197844,0.004954721,0.0009721599,0.008566384,0.001175804,0.01525799],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1552698,"threshold_uncertainty_score":0.3098915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1979033542239544,"score_gpt":0.4007734245904484,"score_spread":0.202870070366494,"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."}}