{"id":"W4408148630","doi":"10.1038/s41467-025-57386-5","title":"A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Lawrence Livermore National Laboratory; Biological and Environmental Research; National Institute of Allergy and Infectious Diseases; Office of Science; National Science Foundation; Lawrence Berkeley National Laboratory; Vetenskapsrådet; Basic Energy Sciences; National Energy Research Scientific Computing Center; Polarforskningssekretariatet; Laboratory Directed Research and Development; U.S. Department of Energy","keywords":"Metagenomics; Ecosystem; Genomics; Biology; Biogeochemistry; Microbiome; Trait; Microbial population biology; Ecology; Ecosystem services; Microbial ecology; Genome; Genetics; Computer science; Gene","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":[0.002597313,0.001458878,0.0009898065,0.001938784,0.0008405253,0.002123636,0.00171319,0.001303463,0.001500093],"category_scores_gemma":[0.003964233,0.0005595685,0.002452901,0.001600893,0.00208743,0.001955864,0.00247864,0.002680595,0.0004011912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001330328,"about_ca_system_score_gemma":0.001926167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01114538,"about_ca_topic_score_gemma":0.0107056,"domain_scores_codex":[0.9990991,0.0005377876,0.00004020933,0.0001850836,0.00009327834,0.0000444488],"domain_scores_gemma":[0.9991131,0.0004587866,0.00008800893,0.0001668815,0.00007805708,0.00009520573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000442718,0.00006997236,0.004561464,0.0003114636,0.0004058625,0.0002452657,0.0002280649,0.4545496,0.007845211,0.5024775,0.001640358,0.02762092],"study_design_scores_gemma":[0.00001473096,0.00005089662,0.001274297,0.0000611446,0.00007502546,0.00009736183,0.00007526705,0.4197275,0.0007697505,0.5631251,0.01467913,0.00004980426],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004064118,0.0004698942,0.9927201,0.0005676991,0.00004501616,0.00002975795,0.0005073023,0.0003127251,0.001283302],"genre_scores_gemma":[0.1999117,0.001441902,0.7950105,0.0007210345,0.000148545,0.0003620144,0.001132189,0.0002162423,0.001055844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01114538,"threshold_uncertainty_score":0.02216101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01160523529484287,"score_gpt":0.2789748976459626,"score_spread":0.2673696623511197,"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."}}