{"id":"W3173084100","doi":"10.1101/2021.06.04.447128","title":"Fine-Scale Adaptations to Environmental Variation and Growth Strategies Drive Phyllosphere <i>Methylobacterium</i> Diversity","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Fermentation and Sensory Analysis","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal; Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; McGill University; National Science Foundation","keywords":"Methylobacterium; Phyllosphere; Biology; Lineage (genetic); Adaptation (eye); Ecology; Phylogenetic tree; Epiphyte; Ecotype; Evolutionary biology; Gene; Bacteria","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":[0.0001360954,0.0002985739,0.0003127649,0.00002792382,0.0004595638,0.0003841921,0.000242046,0.0002293642,0.000388031],"category_scores_gemma":[0.00003123434,0.0001847374,0.0001264309,0.0003332197,0.00006604865,0.0002665992,0.0006575978,0.0002429108,0.00002640232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001040034,"about_ca_system_score_gemma":0.00003703631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005252921,"about_ca_topic_score_gemma":0.0003056501,"domain_scores_codex":[0.9983221,0.000162367,0.0002641524,0.0007120045,0.0002874242,0.0002519687],"domain_scores_gemma":[0.999253,0.00006470449,0.0001771535,0.0001516334,0.0001267936,0.0002267102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001297553,0.0001008511,0.03448431,0.00002021116,0.00008095695,0.00001000611,0.0001279799,0.00006260169,0.9649857,0.00007295052,0.00002702316,0.00001439604],"study_design_scores_gemma":[0.0001126771,0.00005218986,0.9678726,0.00003438751,0.0001202435,1.082865e-8,0.0003202535,0.0001007447,0.03085339,0.000004714477,0.000144117,0.0003846326],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980708,0.0001292566,0.0002605186,0.0005222667,0.0001824634,0.000288229,0.0004394109,0.00009666775,0.00001034358],"genre_scores_gemma":[0.9976421,0.0001686572,0.001691204,0.0002859616,0.0001566789,0.00003359267,0.000008098156,0.000003913485,0.000009766528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9341323,"threshold_uncertainty_score":0.7533374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01174837083940305,"score_gpt":0.18220418227515,"score_spread":0.170455811435747,"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."}}