{"id":"W1766564823","doi":"10.25336/p69k6x","title":"Back to the future: A review of forty years of population projections at Statistics Canada","year":2015,"lang":"en","type":"review","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Demographic statistics; Projections of population growth; Population; Population projection; Population statistics; Regional science; Geography; Demographic analysis; Fertility; Population growth; Statistics; Research methodology; Demography; Econometrics; Sociology; Economics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.001320921,0.0001934311,0.000990933,0.0003162076,0.0002324551,0.000008783069,0.000290696,0.000104985,0.00003187231],"category_scores_gemma":[0.0004530372,0.0001683871,0.0001126118,0.001628321,0.0001085326,0.00005657961,0.00006445282,0.0001355283,0.000005162681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003725263,"about_ca_system_score_gemma":0.001412318,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9864029,"about_ca_topic_score_gemma":0.9998748,"domain_scores_codex":[0.9974413,0.0004611109,0.0009089229,0.0002594736,0.0006098301,0.0003193681],"domain_scores_gemma":[0.9984057,0.00009267752,0.0006181686,0.0003792162,0.0003610525,0.0001431961],"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":[0.000001808915,0.00001124443,0.01376559,0.02983503,0.0002212161,0.000005689362,0.001437607,0.00002755064,3.580886e-10,0.005536239,0.2658166,0.6833414],"study_design_scores_gemma":[0.00003341311,0.000009530751,0.01995854,0.008315872,0.0002553441,4.849097e-7,0.0006322966,8.365129e-7,8.009727e-10,0.000108774,0.9705287,0.0001561877],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00006988721,0.9927671,0.000002207705,0.0001964644,0.001801745,0.002729282,0.001143498,0.000004883714,0.001284908],"genre_scores_gemma":[0.0004920513,0.9978019,0.0001847762,0.0001532383,0.0002400719,0.0001454815,0.0006395136,0.00001947395,0.0003234762],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7047121,"threshold_uncertainty_score":0.9741432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09501216308650197,"score_gpt":0.4093123397832724,"score_spread":0.3143001766967704,"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."}}