{"id":"W7115018318","doi":"10.5281/zenodo.17912386","title":"Urban Data Analytics, Visualization, and Storytelling","year":2025,"lang":"en","type":"book","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":"University of Toronto","funders":"","keywords":"Craft; Storytelling; Variety (cybernetics); Key (lock); Narrative; Urban computing","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":[0.0004535494,0.001322886,0.0006805268,0.002069478,0.001020216,0.005991085,0.001334564,0.0007390629,0.08404296],"category_scores_gemma":[0.001772311,0.0007088353,0.0006134698,0.005015,0.001096571,0.002957373,0.002154466,0.001513509,0.02989721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001839751,"about_ca_system_score_gemma":0.001638026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005450864,"about_ca_topic_score_gemma":0.01072591,"domain_scores_codex":[0.9996547,0.00005797619,0.00001772875,0.00006370134,0.000177419,0.00002842542],"domain_scores_gemma":[0.9992224,0.0003635736,0.00003265968,0.00009836913,0.0001817018,0.0001013276],"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":[0.00001491653,0.00002670572,0.0001273824,0.000372183,0.000005540098,0.0001076597,0.001214997,0.001806917,0.0008186369,0.0618954,0.7433001,0.1903095],"study_design_scores_gemma":[0.000002365694,0.000004960234,0.0001814877,0.0001048912,0.000001602913,0.0001223067,0.0001580634,0.000781988,0.0002354356,0.01437894,0.9840199,0.000008196188],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.003256085,0.0149508,0.2650372,0.00580389,0.003029993,0.0007451343,0.01106054,0.01250007,0.6836164],"genre_scores_gemma":[0.01416005,0.01260094,0.1232485,0.001358528,0.0007545742,0.0006362328,0.01257878,0.006104845,0.8285576],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.08404296,"threshold_uncertainty_score":0.2811517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07161457888270659,"score_gpt":0.2962739931513268,"score_spread":0.2246594142686202,"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."}}