{"id":"W6884654692","doi":"10.11575/prism/30521","title":"VisLink: Revealing Relationships Amongst Visualizations","year":2007,"lang":"en","type":"other","venue":"PRISM (University of Calgary)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Pennsylvania","keywords":"Visualization; Information visualization; Encoding (memory); Reuse; Data visualization","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001747732,0.001693442,0.0006820055,0.004149427,0.001179541,0.00666304,0.001886695,0.001114334,0.0446653],"category_scores_gemma":[0.007192766,0.0009023366,0.001005464,0.003264289,0.0009727005,0.006036886,0.007231781,0.001687579,0.01018111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008033293,"about_ca_system_score_gemma":0.001256699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003691716,"about_ca_topic_score_gemma":0.006340874,"domain_scores_codex":[0.9984731,0.0003742294,0.00007571001,0.000346616,0.0006248856,0.0001054335],"domain_scores_gemma":[0.9970627,0.001232121,0.0001938196,0.0009045703,0.0003848851,0.0002217828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005502094,0.0001521127,0.00235794,0.001133932,0.0001133522,0.0009162196,0.003615598,0.007281114,0.02449433,0.1202561,0.1671484,0.6719807],"study_design_scores_gemma":[0.000180283,0.00009266096,0.002408829,0.0003933113,0.00009530739,0.001041569,0.00141342,0.08829479,0.04256957,0.1308925,0.7324287,0.0001891189],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.009830687,0.0005754335,0.8912545,0.0005837544,0.0001791197,0.0001955209,0.004675677,0.05830899,0.03439631],"genre_scores_gemma":[0.08903915,0.001162334,0.8298872,0.0002838959,0.0001041774,0.0003962796,0.01254954,0.02221936,0.04435815],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.0446653,"threshold_uncertainty_score":0.1494203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0251224176059752,"score_gpt":0.2422237785848098,"score_spread":0.2171013609788346,"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."}}