{"id":"W2132317457","doi":"10.1109/tvcg.2009.162","title":"Lark: Coordinating Co-located Collaboration with Information Visualization","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Workspace; Computer science; Visualization; Flexibility (engineering); Human–computer interaction; Representation (politics); Focus (optics); Information visualization; Pipeline (software); Data visualization; Data science; World Wide Web; Data mining; 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":[0.005244225,0.001447619,0.001419855,0.001714084,0.001547901,0.005342057,0.004387595,0.001680002,0.01234169],"category_scores_gemma":[0.01415247,0.001151815,0.0009638196,0.001221356,0.001570337,0.006018768,0.01417489,0.001895436,0.005399771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009521645,"about_ca_system_score_gemma":0.002301832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002733066,"about_ca_topic_score_gemma":0.003499816,"domain_scores_codex":[0.9937093,0.002383151,0.0003980973,0.001381532,0.001575649,0.0005521873],"domain_scores_gemma":[0.9908167,0.00327192,0.0007097641,0.003151562,0.0009405259,0.001109502],"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":[0.00507505,0.0008738346,0.006078356,0.001726097,0.0003695979,0.001473708,0.01119517,0.02862358,0.09251653,0.03074218,0.06971795,0.7516081],"study_design_scores_gemma":[0.001753746,0.001349636,0.007355217,0.0004627301,0.0004005157,0.001730994,0.003137341,0.6157844,0.09866454,0.0543947,0.2141123,0.0008537456],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02749277,0.0003764607,0.8602095,0.0004359946,0.0001234284,0.000537934,0.0006267411,0.1023305,0.0078667],"genre_scores_gemma":[0.2477386,0.0003027835,0.7356245,0.000277464,0.00009684994,0.0008156372,0.001593528,0.003848084,0.009702513],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01234169,"threshold_uncertainty_score":0.041287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01165494144871717,"score_gpt":0.2819530208665528,"score_spread":0.2702980794178357,"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."}}