{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008003038,0.0001631218,0.0003170106,0.00002684058,0.0003402088,0.0001380881,0.0004070502,0.0001750061,0.00002931201],"category_scores_gemma":[0.0001885469,0.0001223603,0.0002254709,0.00001150764,0.00006464098,0.00001516572,0.0001562244,0.00009893219,0.000003033201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002186905,"about_ca_system_score_gemma":0.00008999338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001232632,"about_ca_topic_score_gemma":0.000002353425,"domain_scores_codex":[0.9987324,0.00006209916,0.0006541156,0.0001538029,0.0001474887,0.000250092],"domain_scores_gemma":[0.998463,0.00002050571,0.0009082074,0.0003606691,0.0001510892,0.00009651867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004825632,0.000124374,0.0008378095,0.00001331829,0.0001372973,0.000002699982,0.00007665288,0.00002722547,0.9915169,0.0006889115,0.002699622,0.003392601],"study_design_scores_gemma":[0.009456927,0.001932232,0.001403011,0.00006337227,0.00004382172,0.00006498116,0.0002105867,0.003112615,0.9418991,0.001155038,0.04007087,0.0005875056],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798306,0.0009892195,0.0180064,0.00006561096,0.0005637813,0.0001952614,0.0000464965,0.000004271225,0.0002983936],"genre_scores_gemma":[0.9727746,0.0001758319,0.02578072,0.0003228632,0.0007086614,0.00001554472,0.00004308579,0.00001644229,0.0001622581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04961788,"threshold_uncertainty_score":0.498971,"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."}}