{"id":"W2897352944","doi":"10.1080/01615440.2018.1507771","title":"The Linkage of Microcensus Data and Vital Records: an Assessment of Results on Quebec Historical Population Data (1852–1911)","year":2018,"lang":"en","type":"article","venue":"Historical Methods A Journal of Quantitative and Interdisciplinary History","topic":"Census and Population Estimation","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Université du Québec à Chicoutimi; Université du Québec à Trois-Rivières","funders":"Concordia University","keywords":"Microdata (statistics); Census; Record linkage; Geography; Linkage (software); Population; Regional science; Genealogy; Demography; History; Sociology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.07424501,0.0006657502,0.000799973,0.007080879,0.003790436,0.003019178,0.002268485,0.0009031043,0.003678908],"category_scores_gemma":[0.1649586,0.000386652,0.001129434,0.01734588,0.002298169,0.002055518,0.003195428,0.0008436933,0.0005883399],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0170512,"about_ca_system_score_gemma":0.013715,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9314418,"about_ca_topic_score_gemma":0.9260611,"domain_scores_codex":[0.9530542,0.03306005,0.00110304,0.002974689,0.00740284,0.002405203],"domain_scores_gemma":[0.840043,0.0869827,0.01181829,0.01478803,0.04391786,0.002450057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008589156,0.0000727474,0.8732837,0.0004662288,0.001815374,0.0005177919,0.009574292,0.006045766,0.0007401361,0.007799948,0.01343532,0.08538985],"study_design_scores_gemma":[0.00004680306,0.0001396406,0.961983,0.0002574787,0.0004740638,0.00012043,0.005425612,0.005473942,0.0008840233,0.0008334303,0.02429581,0.00006579494],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8949122,0.009902704,0.02551078,0.009848385,0.0002707219,0.0008238991,0.02329264,0.0004987554,0.03493989],"genre_scores_gemma":[0.9701161,0.0011407,0.0128324,0.0008283732,0.00006134985,0.0002813959,0.008446631,0.0001497935,0.006143279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9829488,"threshold_uncertainty_score":0.3926498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2987526556050639,"score_gpt":0.5250482692353082,"score_spread":0.2262956136302444,"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."}}