{"id":"W3006723981","doi":"10.1101/2020.02.28.968941","title":"Metabolic multi-stability and hysteresis in a model aerobe-anaerobe microbiome community","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Office; Division of Emerging Frontiers; Multidisciplinary University Research Initiative; Natural Sciences and Engineering Research Council of Canada; California Institute of Technology; Chalmers Tekniska Högskola; Division of Emerging Frontiers in Research and Innovation; National Science Foundation","keywords":"Microbiome; Biology; Transcriptome; Computational biology; Mechanism (biology); Microbiology; Bioinformatics; Gene; Genetics; Gene expression; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006309033,0.0003443725,0.0006017043,0.0006835043,0.0006474356,0.001192366,0.0007355886,0.001161089,0.00146612],"category_scores_gemma":[0.001808282,0.000227672,0.0005790797,0.0004261959,0.0008589269,0.0008739463,0.0009916745,0.0004431506,0.0001489168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009083902,"about_ca_system_score_gemma":0.0006421473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008428258,"about_ca_topic_score_gemma":0.003138064,"domain_scores_codex":[0.999786,0.00007268527,0.000008335614,0.00006429758,0.00002945372,0.00003933533],"domain_scores_gemma":[0.9992836,0.0003467892,0.0001283783,0.0000562857,0.00007015646,0.0001147223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003132257,0.0002301207,0.0243364,0.0001257069,0.0001112722,0.0006509055,0.0003313359,0.9132192,0.02601266,0.030179,0.0009061581,0.003583908],"study_design_scores_gemma":[0.00002439427,0.00005087616,0.002097785,0.000003340869,0.000008756353,0.00003215661,0.00007233333,0.9899609,0.0005621251,0.006929542,0.0002421594,0.00001564621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9739813,0.0001197108,0.02252615,0.0006089252,0.00002750134,0.00002922713,0.0003586086,0.00008984397,0.002258587],"genre_scores_gemma":[0.9950649,0.00005332179,0.003772421,0.00004869435,0.000009772326,0.00003824911,0.0001078485,0.0000100654,0.0008948402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008428258,"threshold_uncertainty_score":0.01675838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04099484417503784,"score_gpt":0.2586627432868743,"score_spread":0.2176678991118365,"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."}}