{"id":"W6912669858","doi":"10.5281/zenodo.3775611","title":"Census program data viewer, 2016 Census","year":2018,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Census; Geospatial analysis; Data visualization; Visualization; Product (mathematics); Presentation (obstetrics); Process (computing); Casual","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007741439,0.0001390315,0.0001281793,0.0001743846,0.001645414,0.001766347,0.004728222,0.00005047765,0.001841948],"category_scores_gemma":[0.000645592,0.0001341986,0.00002929692,0.001008642,0.000228413,0.0007704906,0.005455066,0.0001318382,0.008920782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005513219,"about_ca_system_score_gemma":0.000007427513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008068694,"about_ca_topic_score_gemma":3.484456e-7,"domain_scores_codex":[0.9979857,0.0002436626,0.000266396,0.0006524502,0.0004407766,0.0004109891],"domain_scores_gemma":[0.9969846,0.00001652062,0.00011308,0.001847283,0.0008122961,0.0002262363],"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":[0.0000054407,0.0001369892,6.361528e-7,0.00001369979,0.00001507183,0.000006820488,0.0001396336,0.000001097606,0.0001265615,0.01875321,0.6563625,0.3244383],"study_design_scores_gemma":[0.0002800247,0.0002057876,0.00006257917,0.00002224965,0.000007226946,0.00004838485,0.00004046306,0.02970819,0.0001105423,0.000212485,0.9691358,0.0001663005],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008407803,0.0003172406,0.6125287,0.005422356,0.001240223,0.001540317,0.003625221,0.008954565,0.3655306],"genre_scores_gemma":[0.5510293,0.00300739,0.2128272,0.01103735,0.007525655,5.406027e-7,0.1642684,0.01204118,0.03826295],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5501885,"threshold_uncertainty_score":0.9996543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1062256077719555,"score_gpt":0.3397700493829557,"score_spread":0.2335444416110002,"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."}}