{"id":"W2900609968","doi":"10.1111/geb.12841","title":"Phylogenetically weighted regression: A method for modelling non‐stationarity on evolutionary trees","year":2018,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Evolution and Paleontology Studies","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of British Columbia","funders":"Division of Environmental Biology","keywords":"Phylogenetic tree; Biology; Clade; Phylogenetic comparative methods; Evergreen; Allometry; Phylogenetics; Ecology; Range (aeronautics); Regression; Deciduous; Evolutionary biology; Statistics; Mathematics","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":[0.01429729,0.001564892,0.001362397,0.002712157,0.0005678629,0.001384316,0.003584636,0.00182912,0.003342243],"category_scores_gemma":[0.0331386,0.000964975,0.002503512,0.003064368,0.001210794,0.002157118,0.002346332,0.003261211,0.001052676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006126103,"about_ca_system_score_gemma":0.0009505389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003924003,"about_ca_topic_score_gemma":0.003202534,"domain_scores_codex":[0.9932361,0.005052453,0.0002470685,0.0008316195,0.0004723608,0.0001604371],"domain_scores_gemma":[0.9702701,0.02478894,0.001946267,0.001720119,0.001049755,0.0002247116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001440908,0.000077359,0.007122647,0.0002117177,0.0007354675,0.0002762205,0.0004889496,0.8069329,0.003174352,0.05518125,0.002016118,0.1236389],"study_design_scores_gemma":[0.000007904416,0.00002621706,0.000352145,0.00001747398,0.00001837301,0.00003216777,0.00001897955,0.9793031,0.0002527244,0.0187457,0.001211173,0.00001407001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002286555,0.00004726378,0.9971964,0.00003989517,0.00001094998,0.00001147857,0.00005617811,0.0002685301,0.00008257623],"genre_scores_gemma":[0.1346219,0.0002271458,0.8618575,0.0001082299,0.00008530585,0.0004331453,0.0005807786,0.000774006,0.001311883],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01429729,"threshold_uncertainty_score":0.07561219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0172975170302757,"score_gpt":0.272118131413163,"score_spread":0.2548206143828873,"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."}}