{"id":"W3199328824","doi":"10.1109/tvcg.2021.3114211","title":"ChartStory: Automated Partitioning, Layout, and Captioning of Charts into Comic-Style Narratives","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Science Foundation of Sri Lanka; Natural Sciences and Engineering Research Council of Canada","keywords":"Comics; Storytelling; Computer science; Narrative; Operationalization; Visualization; Pipeline (software); Closed captioning; Data visualization; Human–computer interaction; Data science; World Wide Web; Artificial intelligence; Programming language","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.006896804,0.003190828,0.0007378426,0.004162475,0.001066455,0.004713472,0.002799102,0.001382789,0.02062513],"category_scores_gemma":[0.05176632,0.001053294,0.00141588,0.002110944,0.001215963,0.005898177,0.004177111,0.001797661,0.006323134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009792771,"about_ca_system_score_gemma":0.00223263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002060498,"about_ca_topic_score_gemma":0.003847515,"domain_scores_codex":[0.9965051,0.001622758,0.0003082029,0.0005793991,0.0008550018,0.0001296763],"domain_scores_gemma":[0.9656389,0.02248204,0.002133241,0.005153198,0.003555667,0.001036982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001191798,0.0003585059,0.005280525,0.003583207,0.0001849185,0.001432298,0.01925995,0.01481375,0.02708526,0.02784135,0.2744484,0.62452],"study_design_scores_gemma":[0.000536132,0.0007702106,0.005586421,0.001701221,0.0001641228,0.001302629,0.008357134,0.2258178,0.06983393,0.04035981,0.6450304,0.0005401282],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03055136,0.0007602651,0.7302918,0.00146856,0.0006180413,0.001812463,0.01200885,0.2070207,0.01546793],"genre_scores_gemma":[0.08164003,0.0005922687,0.8799186,0.0003158649,0.0001627858,0.001734327,0.0150527,0.01099683,0.009586628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02062513,"threshold_uncertainty_score":0.06899798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01863847029110049,"score_gpt":0.2833813368248636,"score_spread":0.2647428665337631,"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."}}