{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006615095,0.002606192,0.001009666,0.002653205,0.002076793,0.004294567,0.003884945,0.002935241,0.0460018],"category_scores_gemma":[0.02938363,0.0009480945,0.001210633,0.001378198,0.0014696,0.009053326,0.01240687,0.002768651,0.01024096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007283359,"about_ca_system_score_gemma":0.001285546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00126141,"about_ca_topic_score_gemma":0.001941733,"domain_scores_codex":[0.9955742,0.002067087,0.0002706521,0.0007172454,0.0009725085,0.0003983713],"domain_scores_gemma":[0.9760526,0.01700323,0.0007938675,0.003193129,0.001444196,0.00151293],"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":[0.004458865,0.001095237,0.006303478,0.004303182,0.0002158231,0.003762502,0.04273559,0.005335963,0.04971315,0.03096736,0.3387074,0.5124015],"study_design_scores_gemma":[0.0008080665,0.0007211263,0.004902223,0.001097749,0.0001658145,0.001049979,0.007134669,0.05194413,0.03746922,0.04867607,0.845413,0.0006179232],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03743562,0.0008180021,0.6687399,0.002737322,0.0009946629,0.001822602,0.009496523,0.2449645,0.03299089],"genre_scores_gemma":[0.3070425,0.0009266976,0.5778537,0.002438741,0.0009850382,0.006593784,0.02275421,0.04217789,0.03922749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0460018,"threshold_uncertainty_score":0.1538913,"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."}}