{"id":"W1856437313","doi":"10.25336/p6d615","title":"Canadian Provincial Population Growth: Fertility, Migration, and Age Structure Effects","year":2009,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Global Health Care Issues","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Fertility; Age structure; Demography; Immigration; Population; Net migration rate; Total fertility rate; Geography; Population structure; Population growth; Projections of population growth; Demographic analysis; Demographic economics; Research methodology; Economics; Family planning; 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":[],"consensus_categories":[],"category_scores_codex":[0.0003398484,0.0002240217,0.0003777089,0.0005490897,0.001046284,0.00001683943,0.00009833404,0.0003327827,0.00001911924],"category_scores_gemma":[0.001190882,0.0002307589,0.00002494962,0.0004508716,0.00003830276,0.000233579,0.00002101017,0.0003894019,0.00001706033],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005272209,"about_ca_system_score_gemma":0.0005402801,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9902105,"about_ca_topic_score_gemma":0.9998974,"domain_scores_codex":[0.9976713,0.0003604935,0.0005661433,0.0003829735,0.0002034914,0.0008156113],"domain_scores_gemma":[0.9986518,0.0001371222,0.0001319715,0.0002148763,0.0002430576,0.0006211466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001571687,0.000003545746,0.9850874,0.0002412377,0.000008273867,0.00004070953,0.004140437,0.000008460422,0.000004826611,0.003533238,0.004314773,0.002601365],"study_design_scores_gemma":[0.0003267826,0.0000548709,0.9806035,0.0002546489,0.00001533146,0.00000154035,0.0006744583,0.00006035409,0.000001248776,0.01683474,0.0009834195,0.0001891544],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906283,0.001238481,0.00000108596,0.00428037,0.001330869,0.00156956,0.00009721969,0.00005245024,0.000801645],"genre_scores_gemma":[0.9959726,0.00004456375,0.0001566021,0.002956773,0.0003024471,0.00003829186,0.00039756,0.00001703871,0.0001140997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0133015,"threshold_uncertainty_score":0.9985464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03719988233463152,"score_gpt":0.4072202399793866,"score_spread":0.370020357644755,"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."}}