{"id":"W3204150480","doi":"10.1109/tvcg.2021.3114841","title":"VizSnippets: Compressing Visualization Bundles Into Representative Previews for Browsing Visualization Collections","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Visualization; Snippet; Computer science; Pipeline (software); Relevance (law); Information retrieval; World Wide Web; Data visualization; Key (lock); Human–computer interaction; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00422602,0.003170714,0.001237145,0.005625568,0.001271098,0.004804944,0.002410465,0.00145015,0.02447973],"category_scores_gemma":[0.03444479,0.00148838,0.001913318,0.003814421,0.001351755,0.007115143,0.004673114,0.002268575,0.009417281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009575416,"about_ca_system_score_gemma":0.002100672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002547421,"about_ca_topic_score_gemma":0.006012694,"domain_scores_codex":[0.9975823,0.0006529,0.00036964,0.0003995743,0.0008500605,0.0001455135],"domain_scores_gemma":[0.9812921,0.008600281,0.001490108,0.003452216,0.004503153,0.0006622365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001825396,0.0003492098,0.006903638,0.005132753,0.0003211673,0.00114299,0.004198915,0.0151016,0.03483862,0.02715053,0.1955766,0.7074586],"study_design_scores_gemma":[0.0004953757,0.00126402,0.008570208,0.001782408,0.0004196624,0.001893416,0.003333926,0.2815027,0.1055069,0.08963285,0.5050048,0.0005937857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0177,0.001058105,0.8271008,0.000978232,0.0005400327,0.002540948,0.01759554,0.1255434,0.006942865],"genre_scores_gemma":[0.03890713,0.0006505021,0.9194287,0.0002799098,0.0001371348,0.001701134,0.01915854,0.01331581,0.006421245],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02447973,"threshold_uncertainty_score":0.08189279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0399864939476833,"score_gpt":0.3469616154669047,"score_spread":0.3069751215192214,"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."}}