{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005005564,0.000087705,0.0002039701,0.0005008428,0.001248663,0.001647159,0.002269169,0.00003599542,0.0000853254],"category_scores_gemma":[0.02308014,0.00007230942,0.00001234996,0.0001970544,0.001279609,0.0008746473,0.0001049642,0.0001743421,0.00001622781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006547233,"about_ca_system_score_gemma":0.003814137,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002196768,"about_ca_topic_score_gemma":0.07517716,"domain_scores_codex":[0.9976408,0.0000397102,0.000590617,0.0002470959,0.001202654,0.0002790877],"domain_scores_gemma":[0.9949468,0.0004201238,0.0007744487,0.0009185273,0.002243902,0.0006962271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003296552,0.00002234697,0.01993066,0.00001543226,0.00003654279,0.0006735951,0.001131388,0.00050829,0.0001087327,0.09664089,0.08838052,0.7925186],"study_design_scores_gemma":[0.0006834508,0.0002246498,0.2335505,0.00006487308,0.00009804971,0.000285018,0.00147076,0.5258318,0.000005583182,0.2014005,0.03604837,0.0003364745],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07289596,0.0002508477,0.9149863,0.001167742,0.002097383,0.00008237034,0.006918357,0.000003560534,0.001597481],"genre_scores_gemma":[0.9149743,0.00008695263,0.08463833,0.00009502988,0.00005057828,1.206921e-7,0.00001263062,0.000007468563,0.0001345302],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8420784,"threshold_uncertainty_score":0.9993892,"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."}}