{"id":"W4400800505","doi":"10.1101/2024.07.18.604082","title":"Shared environments complicate the use of strain-resolved metagenomics to infer microbiome transmission","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"Max-Planck-Institut für Evolutionäre Anthropologie; National Commission for Science, Technology and Innovation; Max-Planck-Gesellschaft; University of Notre Dame; Princeton University; National Institutes of Health; National Science Foundation","keywords":"Microbiome; Metagenomics; Transmission (telecommunications); Biology; Strain (injury); Similarity (geometry); Population; Genetics; Evolutionary biology; Computer science; Gene; Artificial intelligence; Environmental health; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01564214,0.00111473,0.001304152,0.001866592,0.001128964,0.003226426,0.001375977,0.001180735,0.001166593],"category_scores_gemma":[0.025385,0.0008353919,0.001198312,0.001955563,0.001508007,0.002011535,0.003061278,0.002525505,0.0005870389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005502931,"about_ca_system_score_gemma":0.0009933988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002296392,"about_ca_topic_score_gemma":0.004146895,"domain_scores_codex":[0.9923859,0.005065406,0.000408794,0.001276566,0.0005768725,0.0002865025],"domain_scores_gemma":[0.981231,0.01044361,0.002379575,0.004409899,0.0009690305,0.0005668813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007311929,0.0002356976,0.7663769,0.001246602,0.005182109,0.0005369265,0.00118848,0.06225092,0.06493289,0.0108013,0.00694652,0.07957055],"study_design_scores_gemma":[0.0001188333,0.0003021166,0.5019652,0.0004883875,0.001240031,0.0007868669,0.002230918,0.2729387,0.02760915,0.1658802,0.02618256,0.000257086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7606823,0.002910923,0.2198005,0.002398796,0.0003776851,0.00009132164,0.008478989,0.001283913,0.003975478],"genre_scores_gemma":[0.916185,0.0006115905,0.07684263,0.0008359326,0.0001982221,0.0001249489,0.00457334,0.0003850833,0.0002432836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01564214,"threshold_uncertainty_score":0.08272457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03234102500313981,"score_gpt":0.2429927942125889,"score_spread":0.2106517692094491,"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."}}