{"id":"W2099272214","doi":"10.1371/journal.pone.0005695","title":"Using Phylogenetic, Functional and Trait Diversity to Understand Patterns of Plant Community Productivity","year":2009,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":712,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"University of California, Santa Barbara; National Science Foundation","keywords":"Phylogenetic diversity; Trait; Phylogenetic tree; Biology; Productivity; Diversity (politics); Evolutionary biology; Functional diversity; Ecology; Genetics; Computer science; Gene; Economics","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":[0.002661515,0.0004249919,0.0003696971,0.002851638,0.0004154201,0.0009416052,0.0003949644,0.0004755773,0.001157704],"category_scores_gemma":[0.004600032,0.0001790464,0.0006028633,0.001559924,0.0006615666,0.001217712,0.0006048195,0.0004877548,0.000146621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000711995,"about_ca_system_score_gemma":0.0003368507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005788578,"about_ca_topic_score_gemma":0.009078802,"domain_scores_codex":[0.9992875,0.000303404,0.00004701838,0.0002122366,0.00009749991,0.00005231446],"domain_scores_gemma":[0.9956324,0.002446153,0.001023189,0.000403287,0.0002593713,0.0002356271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001296744,0.0001053206,0.9703978,0.00007901136,0.000454793,0.00003514111,0.0002582325,0.002851902,0.01031028,0.0002613012,0.00007378736,0.01504269],"study_design_scores_gemma":[0.00000609911,0.0001009448,0.9833203,0.0000118148,0.00004992204,0.00005703337,0.0001331703,0.01447325,0.0007010312,0.0009585106,0.0001757132,0.00001229463],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898662,0.0002609139,0.008921628,0.00004300222,0.00000314053,0.00001923362,0.0003886312,0.00002869438,0.0004684859],"genre_scores_gemma":[0.9946048,0.00005335923,0.004942138,0.00001907875,0.000004358438,0.00002076011,0.0002735696,0.000006088387,0.00007583218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005788578,"threshold_uncertainty_score":0.01407558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1063670868836193,"score_gpt":0.2273021517779218,"score_spread":0.1209350648943026,"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."}}