{"id":"W2809097257","doi":"10.1101/350991","title":"Memote: A community driven effort towards a standardized genome-scale metabolic model test suite","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Biological and Environmental Research; Novo Nordisk Fonden; Norges Forskningsråd; National Institute of General Medical Sciences; Novo Nordisk; Bundesministerium für Bildung und Forschung; Advanced Scientific Computing Research; German Network for Bioinformatics Infrastructure; European Commission; U.S. Department of Energy; Innovationsfonden; W. M. Keck Foundation; Ministry of Science and ICT, South Korea; Knut och Alice Wallenbergs Stiftelse; National Research Foundation; Eli Lilly and Company","keywords":"Workflow; Interoperability; Suite; Software; Set (abstract data type); Conceptual model; Test suite; Software quality; Measure (data warehouse)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001182865,0.0007809509,0.0008395531,0.0002092273,0.0003322115,0.000170557,0.0009391824,0.0009108187,0.00001922028],"category_scores_gemma":[0.0003630514,0.0008140225,0.0003468177,0.0003384656,0.0002493269,0.00001335991,0.00113908,0.001046583,0.00002717852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000109247,"about_ca_system_score_gemma":0.0007124922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001216826,"about_ca_topic_score_gemma":0.00001422653,"domain_scores_codex":[0.9970546,0.000179672,0.0006117668,0.001084024,0.0003600187,0.0007099259],"domain_scores_gemma":[0.9960648,0.000007386263,0.0003529813,0.002467734,0.0007871011,0.0003199932],"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":[0.00006471854,0.00019373,0.0004307132,0.0002397594,0.0002483,0.000002573835,0.00002049019,0.001159133,0.9970886,0.000009832127,0.000535434,0.000006734784],"study_design_scores_gemma":[0.0007643772,0.0001441172,0.01362067,0.0001147211,0.0002772749,1.270039e-7,0.000002781843,0.001388681,0.9517699,0.000002633624,0.03087786,0.00103684],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9773452,0.002028158,0.01704971,0.0001081172,0.001031792,0.00081206,0.001329338,0.0002719036,0.00002371417],"genre_scores_gemma":[0.9755637,0.001433139,0.02061662,0.0001569554,0.001843904,0.0001286588,0.00001387032,0.0002014717,0.0000416585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04531866,"threshold_uncertainty_score":0.9994311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01080004029049756,"score_gpt":0.2209074024452837,"score_spread":0.2101073621547862,"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."}}