{"id":"W4246747826","doi":"10.31235/osf.io/a3gtd","title":"Visualizing demographic evolution using geographically inconsistent census data","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Census; Data science; Geography; Consistency (knowledge bases); American Community Survey; Representation (politics); Analytics; Cluster analysis; Computer science; Visual analytics; Cartography; Visualization; Data mining; Regional science; Demography; Population; Sociology; Artificial intelligence; Political science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000814846,0.0003351644,0.0002058814,0.004696533,0.0004524032,0.001376133,0.000372179,0.0004432347,0.003776983],"category_scores_gemma":[0.005135195,0.0001776128,0.0003307683,0.003651678,0.000303664,0.0009530231,0.001288894,0.0005376993,0.0004982309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000794994,"about_ca_system_score_gemma":0.0007176142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03847915,"about_ca_topic_score_gemma":0.07173082,"domain_scores_codex":[0.9997193,0.0001138027,0.00001500858,0.00005324284,0.00007251952,0.00002605735],"domain_scores_gemma":[0.9980768,0.001025668,0.0002352583,0.0001569788,0.0003386126,0.0001666755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007691709,0.0003067511,0.2626,0.001008023,0.0003970246,0.002077112,0.02331113,0.251381,0.01777986,0.04640035,0.08099476,0.3129747],"study_design_scores_gemma":[0.00008384321,0.00007485449,0.1722139,0.0003326313,0.00009678544,0.0004187083,0.01117913,0.6859963,0.006236168,0.03036352,0.09285586,0.000148287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7710663,0.0008979253,0.1576153,0.004039252,0.0002099092,0.0001834646,0.04150877,0.005688335,0.0187907],"genre_scores_gemma":[0.8885083,0.0003820586,0.09905632,0.00008485257,0.00004428849,0.00008022747,0.009385438,0.000365262,0.002093327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03847915,"threshold_uncertainty_score":0.07651037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.150852528832265,"score_gpt":0.4097564019888238,"score_spread":0.2589038731565588,"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."}}