{"id":"W2157525828","doi":"10.1109/tvcg.2013.137","title":"Automatic Layout of Structured Hierarchical Reports","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Table (database); Tweaking; Readability; Column (typography); Information retrieval; Field (mathematics); Software; Data mining; Database; Computer graphics (images); Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001363586,0.0001739167,0.0002260007,0.000398682,0.000159553,0.0002078913,0.0002503909,0.00009919632,0.0000848616],"category_scores_gemma":[0.000004767684,0.0001603477,0.00008378131,0.000827565,0.0001042168,0.0004891346,0.000008310532,0.0001269443,0.000007860665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001024615,"about_ca_system_score_gemma":0.00004431482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002088851,"about_ca_topic_score_gemma":0.00000625184,"domain_scores_codex":[0.9985094,0.0001051204,0.000501239,0.0003576003,0.0003535529,0.0001730691],"domain_scores_gemma":[0.9989529,0.00007138825,0.0001800096,0.0004501839,0.0002013201,0.0001441678],"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":[0.000002931621,0.0003760811,0.0002887749,0.0001104029,0.00009138523,0.000008339572,0.001189428,0.0007994595,0.00008209253,0.9514253,0.001160779,0.04446508],"study_design_scores_gemma":[0.0002339922,0.0001282118,0.001194122,0.00003835417,0.00001584943,0.00003802118,0.00001490278,0.9923205,0.001237131,0.004274089,0.0003288969,0.0001759001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02720481,0.00001248515,0.9717984,0.00007141243,0.0004402351,0.0002074561,0.000006465329,0.0002178873,0.00004082668],"genre_scores_gemma":[0.9935852,0.00004802742,0.005275195,0.0009727494,0.00002446602,0.00001339713,0.00001398728,0.00001428186,0.00005271826],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9915211,"threshold_uncertainty_score":0.6538792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01389112057517796,"score_gpt":0.2695711887560369,"score_spread":0.2556800681808589,"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."}}