{"id":"W2013487313","doi":"10.1371/journal.pcbi.1000472","title":"On the Accessibility of Adaptive Phenotypes of a Bacterial Metabolic Network","year":2009,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Kavli Institute for Theoretical Physics, University of California, Santa Barbara; Natural Sciences and Engineering Research Council of Canada; University of California, San Diego; James S. McDonnell Foundation; Advanced Research Projects Agency; Defense Advanced Research Projects Agency; Alfred P. Sloan Foundation","keywords":"Phenotype; Adaptation (eye); Biology; Population; Evolutionary biology; Phenotypic plasticity; Genotype; Adaptive evolution; Flux (metallurgy); Genetics; Computational biology; Ecology; Gene; Neuroscience; Chemistry","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":[0.0001439398,0.00007873942,0.0001500234,0.00002019849,0.00002652928,0.000002220135,0.0001166421,0.00006779371,0.00002529512],"category_scores_gemma":[0.0001184993,0.00005303428,0.0000540337,0.00008602392,0.00007794475,0.000001491816,0.00002310611,0.00004650054,0.000001559247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002259546,"about_ca_system_score_gemma":0.00003050261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003242717,"about_ca_topic_score_gemma":8.739599e-7,"domain_scores_codex":[0.9994183,0.00008512731,0.000183697,0.0001716159,0.00004738335,0.00009394361],"domain_scores_gemma":[0.9995989,0.00002904379,0.0001093771,0.0001372137,0.0001107362,0.00001468508],"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":[0.0002579418,0.0001080453,0.0001274935,0.000004065232,0.00007694022,3.616973e-8,0.00001972479,0.01038107,0.954861,0.03174119,0.0002856265,0.002136832],"study_design_scores_gemma":[0.0006882941,0.001512073,0.05303201,0.00002829703,0.00006631357,0.000004571035,0.00001399168,0.001543476,0.8993033,0.04112828,0.002433383,0.0002460239],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954122,0.00038247,0.003578976,0.0001673905,0.0001615345,0.000112485,0.00003184376,0.000005891002,0.0001471573],"genre_scores_gemma":[0.9971902,0.00001916038,0.002168098,0.0001137106,0.0003868568,0.000003006847,0.0001057187,0.000003524852,0.000009719845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05555775,"threshold_uncertainty_score":0.2162675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01347162564377536,"score_gpt":0.2380069403314466,"score_spread":0.2245353146876712,"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."}}