{"id":"W4287668439","doi":"10.48550/arxiv.2009.05936","title":"Geo-Spatial Data Visualization and Critical Metrics Predictions for\\n Canadian Elections","year":2020,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Visualization; Geospatial analysis; Computer science; Data science; Process (computing); Traverse; Interpretation (philosophy); Data visualization; Information visualization; Creative visualization; Data mining; Information retrieval; World Wide Web; Geography; Cartography","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":[0.001432989,0.0005809587,0.0002072769,0.005811408,0.0009700079,0.002933653,0.0007277469,0.0004362366,0.008746968],"category_scores_gemma":[0.007351385,0.000198197,0.0005022618,0.006639201,0.0004552361,0.001079735,0.001128608,0.0007477608,0.0009311315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005994798,"about_ca_system_score_gemma":0.007966425,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7437013,"about_ca_topic_score_gemma":0.8191141,"domain_scores_codex":[0.9993937,0.0001058352,0.00002810332,0.0000889291,0.0002838984,0.00009955143],"domain_scores_gemma":[0.9982076,0.0006224741,0.0001496593,0.0001599121,0.0007037375,0.0001566093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004037488,0.0001638143,0.09257427,0.0009204779,0.0001513794,0.0008609045,0.006460942,0.07428464,0.004559435,0.1126309,0.3136889,0.3933006],"study_design_scores_gemma":[0.00005344619,0.00004791319,0.1350153,0.0005086099,0.00008629081,0.000245905,0.007453447,0.4168019,0.007487222,0.03402912,0.398056,0.0002147642],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4493493,0.004135588,0.161433,0.0158512,0.0007858032,0.0006165049,0.1634057,0.03747031,0.1669527],"genre_scores_gemma":[0.8076094,0.001725913,0.1368031,0.0001777928,0.00007707937,0.0001634622,0.03630152,0.001267626,0.01587417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2562987,"threshold_uncertainty_score":0.5156162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1761569196610239,"score_gpt":0.2748119488850941,"score_spread":0.09865502922407018,"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."}}