{"id":"W2132524713","doi":"10.1890/15-0086.1","title":"Improving phylogenetic regression under complex evolutionary models","year":2015,"lang":"en","type":"article","venue":"Ecology","topic":"Evolution and Paleontology Studies","field":"Earth and Planetary Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Phylogenetic tree; Phylogenetic comparative methods; Null model; Regression; Type I and type II errors; Covariance; Statistics; Biology; Evolutionary biology; Econometrics; Mathematics; Ecology","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":[0.02430881,0.001780947,0.002205363,0.001945298,0.0009178944,0.001629786,0.002455093,0.001715202,0.002346613],"category_scores_gemma":[0.08123802,0.0009595088,0.002035759,0.002230535,0.001726186,0.003089974,0.003715047,0.002857268,0.0009101632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009295536,"about_ca_system_score_gemma":0.001784555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005731573,"about_ca_topic_score_gemma":0.003603606,"domain_scores_codex":[0.9874759,0.009692411,0.0003607609,0.001415656,0.0007031307,0.0003521274],"domain_scores_gemma":[0.9209787,0.07015535,0.002459042,0.003391152,0.002505883,0.000509912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001898057,0.00008458213,0.01207561,0.0001958064,0.0003615933,0.0002170755,0.0003963578,0.8412987,0.002843598,0.02036234,0.001296638,0.1206778],"study_design_scores_gemma":[0.00001224214,0.00003710279,0.0005953643,0.000008962988,0.00001918349,0.00002897303,0.00002070412,0.9870189,0.0003966812,0.01146627,0.0003857278,0.000009800454],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02849124,0.0002679095,0.9696565,0.0002787349,0.00003017911,0.00002344962,0.00005327922,0.0007745811,0.0004241461],"genre_scores_gemma":[0.4540579,0.0005024866,0.5415596,0.0003378827,0.0001769999,0.0001533768,0.00053585,0.000894677,0.001781292],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02430881,"threshold_uncertainty_score":0.1285588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08251411592617477,"score_gpt":0.2634323456055431,"score_spread":0.1809182296793683,"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."}}