{"id":"W3128783651","doi":"10.1109/vis47514.2020.00040","title":"Gaze-Driven Links for Magazine Style Narrative Visualizations","year":2020,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Narrative; Gaze; Human–computer interaction; Modalities; Visualization; Reading (process); Style (visual arts); Eye tracking; Comprehension; Information visualization; Reading comprehension; Multimedia; World Wide Web; Artificial intelligence; Linguistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004579886,0.00008956668,0.0001135735,0.00004265173,0.0001236508,0.0001750456,0.0004722335,0.00005443209,0.000131331],"category_scores_gemma":[0.0001041701,0.00008015156,0.00004919851,0.0004962179,0.00002137343,0.0003936623,0.0001474261,0.00006274482,0.0001359621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007201137,"about_ca_system_score_gemma":0.00005230615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.350522e-7,"about_ca_topic_score_gemma":0.000006476046,"domain_scores_codex":[0.9992464,0.00002511128,0.0001835487,0.0002679897,0.000133935,0.0001429793],"domain_scores_gemma":[0.9994345,0.00004923157,0.00005594313,0.0002012055,0.000120809,0.0001383489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003705617,0.0000799239,0.000146406,0.00002577091,0.00003005641,0.000002006933,0.006465656,0.0007521588,0.001068718,0.7271166,0.2626451,0.001663793],"study_design_scores_gemma":[0.0002896662,0.00009478915,0.0000314934,0.000004500456,0.000005161318,5.602632e-7,0.0001966437,0.8429133,0.0006514644,0.0003632556,0.1553345,0.0001147434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00006411387,0.000008863502,0.9857308,0.009372114,0.00007606403,0.0001703345,0.00002831697,0.0002541278,0.00429529],"genre_scores_gemma":[0.5094951,0.00005798753,0.4208553,0.05562079,0.0005563512,0.00007232006,0.0007532366,0.00005419877,0.01253473],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8421611,"threshold_uncertainty_score":0.3268486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04445791319056748,"score_gpt":0.3315430015663245,"score_spread":0.2870850883757571,"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."}}