{"id":"W2277442431","doi":"10.11575/prism/29892","title":"The 2006 Canadian Census Hierarchical PUMF","year":2012,"lang":"en","type":"article","venue":"PRISM (University of Calgary)","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Microdata (statistics); Hierarchy; American Community Survey; Public use; Data file; Geography; Computer science; Data science; Database; Demography; Population; Sociology; Political science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002211585,0.0008214332,0.0007295082,0.01247852,0.003839067,0.002507825,0.00231202,0.0004966517,0.03235867],"category_scores_gemma":[0.014914,0.0006933259,0.0006944011,0.03327863,0.0004606327,0.001161693,0.001248222,0.00104475,0.008144989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04924702,"about_ca_system_score_gemma":0.1261088,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9959993,"about_ca_topic_score_gemma":0.996265,"domain_scores_codex":[0.9957944,0.0001915589,0.0002164717,0.0003271748,0.002657162,0.0008131933],"domain_scores_gemma":[0.9825637,0.0005373824,0.0008471402,0.0007558227,0.01455569,0.000740248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009363557,0.00004085282,0.0319093,0.0003510557,0.00003992543,0.00006802032,0.0005763147,0.001151248,0.0001853207,0.01211019,0.8994917,0.05398245],"study_design_scores_gemma":[0.00002441056,0.00001301805,0.1999097,0.0002389687,0.00002952458,0.00007913986,0.0009372144,0.001825451,0.0004361131,0.0008525654,0.7955744,0.00007949182],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005749163,0.0002810727,0.001235189,0.0005840506,0.00009222662,0.000335066,0.9688275,0.0004285823,0.02246704],"genre_scores_gemma":[0.04838507,0.001456562,0.0109271,0.000521971,0.00006320606,0.001357516,0.9002503,0.0003427654,0.03669557],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04924702,"threshold_uncertainty_score":0.3573139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04981617454293088,"score_gpt":0.2705143206505255,"score_spread":0.2206981461075946,"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."}}