{"id":"W3083783622","doi":"10.1111/geb.13179","title":"Global root traits (GRooT) database","year":2020,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Soil Carbon and Nitrogen Dynamics","field":"Agricultural and Biological Sciences","cited_by":218,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Natural Resources Canada; University of Saskatchewan; The Scarborough Hospital; University of Toronto; Canadian Forest Service","funders":"Biological and Environmental Research; Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Russian Science Foundation; Office of Science; Robert Schalkenbach Foundation; Agence Nationale de la Recherche; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Deutsche Forschungsgemeinschaft; U.S. Department of Energy","keywords":"Trait; Biome; Root (linguistics); Biology; Ecology; Subspecies; Taxonomic rank; Taxon; Database; Geography; Ecosystem; Computer science","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":[0.001090929,0.001613411,0.001264847,0.007266994,0.0004069219,0.001790833,0.002002693,0.00121262,0.017932],"category_scores_gemma":[0.004460556,0.000508283,0.0009428904,0.007110924,0.0003058501,0.001499452,0.002526131,0.0008370597,0.01748527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006480177,"about_ca_system_score_gemma":0.001266303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003561451,"about_ca_topic_score_gemma":0.004849585,"domain_scores_codex":[0.9989178,0.000117646,0.0002731622,0.0003449942,0.0002275874,0.0001187969],"domain_scores_gemma":[0.9975924,0.0006875415,0.0005486412,0.000443817,0.0005022276,0.0002253916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003458719,0.0003004321,0.05032751,0.01772887,0.0008245851,0.001883696,0.0009917761,0.007169865,0.02213219,0.01745795,0.7345293,0.1431951],"study_design_scores_gemma":[0.000486101,0.0001913882,0.06934881,0.001022391,0.0003078188,0.0008386174,0.0003832719,0.006754398,0.007039594,0.008994444,0.9044558,0.0001773713],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005926869,0.0004417379,0.003145915,0.00004629333,0.00001841293,0.00007637402,0.983198,0.00499032,0.002156002],"genre_scores_gemma":[0.008191347,0.0002186192,0.006490517,0.00006167197,0.000009846523,0.0001830435,0.9836975,0.0006306055,0.0005168674],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.017932,"threshold_uncertainty_score":0.0599885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01101888499565406,"score_gpt":0.2095751526160201,"score_spread":0.198556267620366,"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."}}