{"id":"W2130247386","doi":"10.1109/tvcg.2010.194","title":"SparkClouds: Visualizing Trends in Tag Clouds","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":168,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Tag cloud; Computer science; Cloud computing; Visualization; Data visualization; Bar chart; Information retrieval; Data science; World Wide Web; Artificial intelligence","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":[0.001388804,0.0009053918,0.0005642247,0.006061026,0.0007258682,0.002516394,0.0007415232,0.0006641048,0.005324126],"category_scores_gemma":[0.006281652,0.0003581198,0.0006807386,0.006756017,0.0003294123,0.003889033,0.001729078,0.00083937,0.001442679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006190882,"about_ca_system_score_gemma":0.0008328148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009137779,"about_ca_topic_score_gemma":0.008749387,"domain_scores_codex":[0.9994113,0.0001278437,0.00007302204,0.0001027866,0.0002258583,0.00005916151],"domain_scores_gemma":[0.9955467,0.002049711,0.0006227099,0.0006069854,0.0008846028,0.0002892486],"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":[0.002975379,0.0004687908,0.04386159,0.00271516,0.000455404,0.001356539,0.0206068,0.05178018,0.04867411,0.0320915,0.1481865,0.6468281],"study_design_scores_gemma":[0.0004365827,0.0005091976,0.04805876,0.0004079373,0.0002504035,0.001169031,0.01214566,0.567479,0.06603845,0.08362642,0.2194571,0.0004214208],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3253984,0.001488956,0.5082888,0.00170472,0.0005756466,0.0005723278,0.05238064,0.09238075,0.01720984],"genre_scores_gemma":[0.583728,0.001053385,0.3778272,0.0001414623,0.0001637336,0.0004066998,0.02752882,0.004242482,0.004908216],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009137779,"threshold_uncertainty_score":0.01816916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02122277414727932,"score_gpt":0.3008814098035279,"score_spread":0.2796586356562486,"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."}}