{"id":"W2105893525","doi":"10.1109/tvcg.2007.70521","title":"VisLink: Revealing Relationships Amongst Visualizations","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":198,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Pennsylvania","keywords":"Computer science; Visualization; Bridging (networking); Data visualization; Reuse; Human–computer interaction; Variety (cybernetics); Information visualization; Encoding (memory); Space (punctuation); Theoretical computer science; Information retrieval; Data mining; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001025072,0.0003109513,0.0002561649,0.0009695724,0.001031398,0.0004668314,0.0004652194,0.0002163702,0.00002179016],"category_scores_gemma":[0.00001917801,0.0003314903,0.000122268,0.002427293,0.0001267792,0.0008809992,0.00001336604,0.0003525042,0.00003540849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004589694,"about_ca_system_score_gemma":0.00005425754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001365256,"about_ca_topic_score_gemma":0.0000665871,"domain_scores_codex":[0.9975157,0.0002059879,0.0007126665,0.0006417102,0.0005220951,0.0004018859],"domain_scores_gemma":[0.9982494,0.0003244351,0.0002114152,0.0005675027,0.000337762,0.0003094477],"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":[0.000008040133,0.0002601086,0.0002790661,0.00002565563,0.00004200612,0.000006264042,0.0008948974,0.0007591797,0.00001874365,0.991686,0.0004413437,0.005578678],"study_design_scores_gemma":[0.0005918076,0.0001473966,0.0009929527,0.00006519884,0.00003806567,0.00002736131,0.0001014215,0.9896212,0.001488967,0.001728,0.004745334,0.0004523179],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002234552,0.00003671019,0.9956411,0.0001073119,0.0007867506,0.0002561118,0.00001601426,0.0006446398,0.000276835],"genre_scores_gemma":[0.9891308,0.0002695239,0.007484261,0.00257203,0.0001329196,0.00001245268,0.00006436954,0.00004570276,0.000287948],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.989958,"threshold_uncertainty_score":0.9999137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03493980262763771,"score_gpt":0.3009453598490365,"score_spread":0.2660055572213988,"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."}}