{"id":"W2971500937","doi":"10.1111/geb.12996","title":"Robustness of trait connections across environmental gradients and growth forms","year":2019,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":140,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"H2020 European Research Council; Australian Research Council; Horizon 2020 Framework Programme; Advanced Research Projects Agency - Energy; Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Russian Science Foundation; U.S. Department of Energy; European Commission; Seventh Framework Programme; University of Minnesota; Advanced Research Projects Agency; National Science Foundation","keywords":"Trait; Biology; Ecology; Woody plant; Temperate climate; Specific leaf area; Robustness (evolution); Resource Acquisition Is Initialization; Arid; Modularity (biology); Evolutionary biology; Botany; Resource allocation","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":[0.004374675,0.000472186,0.0006434056,0.002037643,0.0006237856,0.001731782,0.0006435792,0.0005662838,0.002556575],"category_scores_gemma":[0.02130795,0.00046453,0.001176895,0.001424972,0.001187981,0.001654783,0.001482749,0.0006881108,0.000333764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000355285,"about_ca_system_score_gemma":0.0002421723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002856537,"about_ca_topic_score_gemma":0.001897288,"domain_scores_codex":[0.9975237,0.0009076619,0.0001670385,0.001094149,0.000149169,0.000158303],"domain_scores_gemma":[0.9728538,0.01911747,0.003766602,0.002901698,0.000716593,0.0006438183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004889673,0.00003880229,0.9543321,0.0001246494,0.001731715,0.0001397219,0.000325154,0.02528,0.005424461,0.001003273,0.0004504892,0.01066061],"study_design_scores_gemma":[0.00002630759,0.0001224579,0.9126547,0.0000340774,0.0003053363,0.0002380467,0.0002843975,0.08043648,0.0009700523,0.004298088,0.0005936567,0.00003646973],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939213,0.0001343301,0.004183875,0.00006935419,0.000006280487,0.000009532178,0.0009746537,0.0001010437,0.000599667],"genre_scores_gemma":[0.9985729,0.0000267941,0.0004154748,0.00001240715,0.000003639393,0.000007598606,0.0008749125,0.00001768079,0.0000684548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004374675,"threshold_uncertainty_score":0.02313572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008940948037041534,"score_gpt":0.1860225408758316,"score_spread":0.1770815928387901,"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."}}