{"id":"W2122306708","doi":"10.1109/iv.2002.1028774","title":"Visualizing metabolic networks in VRML","year":2003,"lang":"en","type":"article","venue":"Proceedings Sixth International Conference on Information Visualisation","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; VRML; Glyph (data visualization); Scripting language; Visualization; Human–computer interaction; Software; Avatar; Data visualization; The Internet; Virtual reality; Multimedia; World Wide Web; Programming language; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005752631,0.0001899227,0.0001416529,0.0002486609,0.00008122394,0.000242451,0.000221649,0.0001741695,0.0001269236],"category_scores_gemma":[0.0001883513,0.0001939358,0.0000569795,0.0001817758,0.00003638005,0.0001513034,0.00004580457,0.0001677973,0.00005249239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005217518,"about_ca_system_score_gemma":0.00005771948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008506317,"about_ca_topic_score_gemma":0.000005508251,"domain_scores_codex":[0.9987028,0.00001951679,0.0005677078,0.0001775476,0.0002864127,0.0002460561],"domain_scores_gemma":[0.9991773,0.000008525058,0.0002997494,0.00009671344,0.0003457128,0.00007200932],"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":[0.0001235714,0.00006154933,0.00520333,0.00002027318,0.00004414948,1.713873e-7,0.00089329,0.001761748,0.002438413,0.9814655,0.001553925,0.006434084],"study_design_scores_gemma":[0.006105732,0.0008581621,0.02018303,0.0003122349,0.00004450226,0.00006493335,0.007129012,0.6015625,0.03484743,0.01782416,0.3090427,0.00202567],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5812207,0.0001010049,0.07110029,0.0006466259,0.001634544,0.0008930021,0.00002853514,0.00008966426,0.3442857],"genre_scores_gemma":[0.9974123,0.00020399,0.0006101772,0.001063117,0.0001564274,0.00006369032,0.0003354188,0.00001181458,0.000143024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9636413,"threshold_uncertainty_score":0.7908474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02453783803161633,"score_gpt":0.2928027134431461,"score_spread":0.2682648754115298,"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."}}