{"id":"W3001738025","doi":"10.1109/mcg.2020.2968906","title":"PixelClipper: Supporting Public Engagement and Conversation About Visualizations","year":2020,"lang":"en","type":"article","venue":"IEEE Computer Graphics and Applications","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of Calgary","funders":"H2020 Marie Skłodowska-Curie Actions; Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Computer science; Facilitator; Visualization; Conversation; Data visualization; Bridge (graph theory); World Wide Web; Public engagement; Information visualization; Human–computer interaction; Function (biology); Annotation; Data science; Multimedia; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002228494,0.0001281996,0.0001309865,0.0001210883,0.0003867805,0.0005598754,0.0003316359,0.00004583724,0.000007641369],"category_scores_gemma":[0.00001230587,0.0001337425,0.00003320519,0.0006421643,0.00008677885,0.0004199781,0.0002264513,0.000107588,0.00001286357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006548682,"about_ca_system_score_gemma":0.00004005345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003195882,"about_ca_topic_score_gemma":0.000002216052,"domain_scores_codex":[0.9988639,0.00005447702,0.0002982914,0.0004303306,0.0001709921,0.0001820537],"domain_scores_gemma":[0.9991496,0.00006872036,0.0001357547,0.0002652891,0.0001374318,0.0002432568],"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":[2.814355e-7,0.00004013678,0.001921754,0.00003055266,0.00001912606,5.474301e-7,0.00059487,0.00002673923,0.00009115913,0.9796153,0.001151905,0.01650764],"study_design_scores_gemma":[0.0002706895,0.00004287946,0.001710414,0.000007403319,0.00001592729,0.000004432836,0.0000522978,0.8461726,0.00006452641,0.003095506,0.1483564,0.0002069024],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002228504,0.00008082012,0.9910805,0.005968421,0.00005876258,0.0002758221,0.00001845432,0.0001945244,0.00009414511],"genre_scores_gemma":[0.9728087,0.0005968619,0.01869469,0.007443066,0.0002426035,0.00007090252,0.0001143278,0.0000147417,0.00001411839],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9765198,"threshold_uncertainty_score":0.545386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05245772478856532,"score_gpt":0.3079893424210478,"score_spread":0.2555316176324824,"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."}}