{"id":"W4411691518","doi":"10.1093/gigascience/giaf072","title":"Integrating comparative genomics and risk classification by assessing virulence, antimicrobial resistance, and plasmid spread in microbial communities with gSpreadComp","year":2025,"lang":"en","type":"article","venue":"GigaScience","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"German Network for Bioinformatics Infrastructure; Fundação de Amparo à Pesquisa do Estado de São Paulo; Deutsche Forschungsgemeinschaft; International Development Research Centre; Alexander von Humboldt-Stiftung","keywords":"Metagenomics; Biology; Virulence; Genomics; Comparative genomics; Genetics; Antibiotic resistance; Workflow; Genome; Horizontal gene transfer; Computational biology; Gene; Computer science; Database","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003082392,0.0001419713,0.0001733782,0.00008342516,0.000355877,0.000148161,0.0001766136,0.00009136674,7.909367e-7],"category_scores_gemma":[0.00001679294,0.0001285029,0.00001273986,0.0001915965,0.0006198995,0.00002387208,0.0001132645,0.0002086855,4.41829e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003466211,"about_ca_system_score_gemma":0.0001920178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003717777,"about_ca_topic_score_gemma":0.004700981,"domain_scores_codex":[0.9990838,0.0001209577,0.0002025944,0.000312681,0.0000536829,0.0002263423],"domain_scores_gemma":[0.9995477,0.00004120986,0.0001201541,0.0001876362,0.00006116462,0.00004210748],"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":[0.00006517621,0.00003236287,0.05062982,0.00004050169,0.000007801012,3.497959e-7,0.0007480009,0.00001047915,0.9468353,0.0001559257,0.001157043,0.0003172377],"study_design_scores_gemma":[0.003067091,0.0003505285,0.4294021,0.001260836,0.00006134831,0.00004378597,0.01403013,0.002228968,0.5267435,0.0001502803,0.02173507,0.0009262804],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955099,0.0005435802,0.002432358,0.0001451779,0.0000485207,0.000181282,0.00005681923,0.000007854361,0.001074574],"genre_scores_gemma":[0.9927961,0.0008355158,0.005903579,0.0002444665,0.00001236529,0.000006553394,0.00005372521,0.000005655693,0.0001420745],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4200917,"threshold_uncertainty_score":0.5240195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0150347525249797,"score_gpt":0.2798090300468543,"score_spread":0.2647742775218745,"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."}}