{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00396119,0.0002754861,0.0004180839,0.0005884005,0.00154541,0.0003715021,0.001520867,0.0006043391,0.0005372053],"category_scores_gemma":[0.0007247592,0.0002840083,0.0003244152,0.0010287,0.001322113,0.0001983615,0.0013751,0.00052455,0.00002598461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003226919,"about_ca_system_score_gemma":0.001160026,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1200942,"about_ca_topic_score_gemma":0.1383636,"domain_scores_codex":[0.9959179,0.0009344792,0.000637414,0.001126202,0.0008768027,0.0005072606],"domain_scores_gemma":[0.9967459,0.0002268,0.0003538068,0.001787199,0.0006311102,0.0002552119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001329044,0.002349027,0.7726724,0.001229365,0.003904348,0.00003658353,0.01270693,0.005669592,0.0003723361,0.1606729,0.008183255,0.03207035],"study_design_scores_gemma":[0.001229899,0.0001791088,0.09512525,0.00203802,0.005745182,0.00000525776,0.02798696,0.570694,0.00002761769,0.1968938,0.09473293,0.005342036],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8198764,0.001897773,0.1528672,0.003061932,0.002136631,0.00166906,0.0004943006,0.0009721147,0.01702458],"genre_scores_gemma":[0.9941965,0.0001808425,0.003719423,0.000184065,0.0008835004,0.000014591,0.0006821071,0.00002071721,0.0001182029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6775472,"threshold_uncertainty_score":0.9999612,"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."}}