{"id":"W2033650322","doi":"10.1109/infovis.2004.53","title":"PhylloTrees: Harnessing Nature&#146;s Phyllotactic Patterns for Tree Layout","year":2004,"lang":"en","type":"article","venue":"IEEE Symposium on Information Visualization","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Tree (set theory); A priori and a posteriori; Theoretical computer science; Mathematics; Combinatorics","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":[0.0007026739,0.0006303098,0.0005201012,0.001234329,0.0006587555,0.001539622,0.0009661231,0.0006849875,0.003311335],"category_scores_gemma":[0.00581078,0.000571957,0.0005515497,0.002147898,0.0009281655,0.002549477,0.001888466,0.001136736,0.00105257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004169237,"about_ca_system_score_gemma":0.0005349903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001255052,"about_ca_topic_score_gemma":0.003623384,"domain_scores_codex":[0.9994325,0.000188097,0.00003426164,0.0001012135,0.0001950887,0.00004881642],"domain_scores_gemma":[0.9965064,0.00161388,0.0003782093,0.001007684,0.0003143999,0.0001793456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005639263,0.0002108588,0.008068529,0.0006216894,0.0001031726,0.0006815766,0.002842928,0.1240471,0.07303535,0.120683,0.02460364,0.6445382],"study_design_scores_gemma":[0.0001255876,0.0002672698,0.002881201,0.0001238399,0.00005202578,0.0009396108,0.000532603,0.7649112,0.03790852,0.1337048,0.0584453,0.0001080689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04421717,0.0004532628,0.947534,0.0004742898,0.00006288168,0.00005980343,0.000556005,0.003502487,0.003140069],"genre_scores_gemma":[0.232379,0.0006828919,0.7613102,0.0002138872,0.00006727988,0.0001950621,0.0009434001,0.001119614,0.003088719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003311335,"threshold_uncertainty_score":0.01107752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01435124205310617,"score_gpt":0.3004182894384841,"score_spread":0.286067047385378,"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."}}