{"id":"W3166851616","doi":"10.1111/ele.13814","title":"Multiple Mutualism Effects generate synergistic selection and strengthen fitness alignment in the interaction between legumes, rhizobia and mycorrhizal fungi","year":2021,"lang":"en","type":"article","venue":"Ecology Letters","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Agriculture","keywords":"Mutualism (biology); Biology; Rhizobia; Medicago truncatula; Trait; Heritability; Coevolution; Ecology; Natural selection; Symbiosis; Evolutionary ecology; Selection (genetic algorithm); Host (biology); Evolutionary biology; Bacteria; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005952859,0.0004042084,0.0002833077,0.000481891,0.0003290733,0.000418294,0.0002049614,0.0002430564,0.001103095],"category_scores_gemma":[0.0006675769,0.000219758,0.0002692195,0.0001789367,0.0004145532,0.0002693235,0.001240379,0.0003281563,0.000117051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003348275,"about_ca_system_score_gemma":0.000203902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003068138,"about_ca_topic_score_gemma":0.001136819,"domain_scores_codex":[0.9994624,0.0002023032,0.00002439152,0.0001096065,0.0001219418,0.00007930645],"domain_scores_gemma":[0.999329,0.0002070116,0.0001644387,0.00008536437,0.0000519097,0.0001623558],"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":[0.0002070461,0.00005318297,0.02569893,0.00005087785,0.0001088598,0.0001369879,0.0001408091,0.000356705,0.9651846,0.0004866918,0.00004314785,0.007532232],"study_design_scores_gemma":[0.00004125728,0.0006970193,0.8922325,0.00001831596,0.0002265659,0.001002711,0.0003988006,0.006755063,0.09514506,0.001574831,0.001853615,0.00005432832],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984095,0.0001632896,0.0008020152,0.00002912075,0.000002277116,0.00000319117,0.00002082296,0.00001735458,0.0005525512],"genre_scores_gemma":[0.9993123,0.00002879674,0.0004874752,0.00001519064,0.000002241791,0.00000317167,0.00001570561,0.000004898829,0.0001302492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001103095,"threshold_uncertainty_score":0.003690243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01995250351193227,"score_gpt":0.204266736169464,"score_spread":0.1843142326575317,"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."}}