{"id":"W2623956974","doi":"","title":"Identifying Households for Historical Censuses to Generate Longitudinal Data","year":2017,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Census and Population Estimation","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Longitudinal data; Geography; Data science; Genealogy; Computer science; History; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000469567,0.0001771951,0.0003676144,0.0001110625,0.0007537216,0.00004154261,0.001223278,0.0001743877,0.00005689162],"category_scores_gemma":[0.0003207916,0.0001779718,0.0001477602,0.00009578841,0.00002889351,0.0002338049,0.0001482576,0.0001428676,0.00001673065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001878624,"about_ca_system_score_gemma":0.0000903593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00119807,"about_ca_topic_score_gemma":0.00288914,"domain_scores_codex":[0.9989375,0.00004160344,0.0002118022,0.0003364532,0.0002902811,0.0001822921],"domain_scores_gemma":[0.997597,0.0001891741,0.0006294937,0.001250796,0.0002658235,0.0000677385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.002173688,0.0004101057,0.003192195,0.00412742,0.0008877195,0.0000441232,0.01437995,0.0002725516,0.01804553,0.03870077,0.8863242,0.0314417],"study_design_scores_gemma":[0.003185682,0.0003437379,0.6645782,0.001055845,0.005199805,0.00002442304,0.005295972,0.008097336,0.0005242084,0.04314212,0.2664914,0.002061324],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9421371,0.0007358174,0.04462986,0.00270883,0.003645272,0.001945733,0.0012335,0.0002008111,0.002763055],"genre_scores_gemma":[0.9464592,0.00007182794,0.01677919,0.00001136843,0.0003435854,0.000001015874,0.002163052,0.0000461356,0.03412466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.661386,"threshold_uncertainty_score":0.7257478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2096704703888092,"score_gpt":0.3530839421855205,"score_spread":0.1434134717967113,"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."}}