{"id":"W2587129898","doi":"10.3791/54869","title":"A Web Tool for Generating High Quality Machine-readable Biological Pathways","year":2017,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Genome Alberta; Genome Canada","keywords":"SBML; Computer science; Function (biology); Replication (statistics); Web server; KEGG; World Wide Web; Markup language; Human–computer interaction; The Internet; XML; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001542431,0.002008979,0.0009750907,0.003274895,0.001013706,0.002429363,0.002605261,0.001227664,0.04526467],"category_scores_gemma":[0.003571506,0.00147869,0.001841754,0.002808681,0.0004762616,0.002208038,0.002411872,0.002474921,0.02361784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001071103,"about_ca_system_score_gemma":0.00142752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001488069,"about_ca_topic_score_gemma":0.001949923,"domain_scores_codex":[0.9993742,0.0001147691,0.00005877819,0.0001565455,0.0002461985,0.00004947609],"domain_scores_gemma":[0.998543,0.0007193781,0.0000983936,0.0002807783,0.0002463802,0.0001120141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001308767,0.0003440298,0.002435599,0.004789728,0.0005453916,0.002372468,0.0009241428,0.03398692,0.1139323,0.04119056,0.4548081,0.3433619],"study_design_scores_gemma":[0.0005921963,0.0001585382,0.002773912,0.0004770108,0.0001414826,0.001532985,0.0002342598,0.1014512,0.1193256,0.05513569,0.7178679,0.0003092935],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.004072429,0.0003929269,0.6087488,0.0002281613,0.0001978048,0.0004243249,0.03983895,0.337958,0.008138585],"genre_scores_gemma":[0.03197099,0.00105465,0.757702,0.0002737709,0.00005329294,0.002367236,0.1464653,0.04934889,0.01076392],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.04526467,"threshold_uncertainty_score":0.1514254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05507275944932859,"score_gpt":0.4104100379705486,"score_spread":0.35533727852122,"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."}}