{"id":"W3035663944","doi":"10.1111/2041-210x.13434","title":"Robustness of linear mixed‐effects models to violations of distributional assumptions","year":2020,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":1352,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Deutsche Forschungsgemeinschaft; National Science Foundation","keywords":"Heteroscedasticity; Random effects model; Statistics; Mathematics; Residual; Econometrics; Variance (accounting); Robustness (evolution); Mixed model; Best linear unbiased prediction; Computer science; Algorithm; Selection (genetic algorithm); Artificial intelligence","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2668743,0.002556775,0.002990147,0.003071384,0.002121438,0.00653754,0.004898625,0.003266761,0.004691823],"category_scores_gemma":[0.6113181,0.00220497,0.005992273,0.002898469,0.006616863,0.00536876,0.004843201,0.00693016,0.001097243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003355259,"about_ca_system_score_gemma":0.002922562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01101593,"about_ca_topic_score_gemma":0.005708557,"domain_scores_codex":[0.7766767,0.1829387,0.009383512,0.01830309,0.01079931,0.001898649],"domain_scores_gemma":[0.21634,0.7207463,0.02055085,0.03231383,0.009116413,0.0009326299],"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":[0.003585833,0.0003162312,0.1081851,0.003283501,0.01818923,0.001868802,0.003349851,0.6446197,0.004273376,0.08288973,0.006965368,0.1224732],"study_design_scores_gemma":[0.0003672918,0.000848768,0.0322694,0.00143186,0.002092878,0.0008244425,0.0008367825,0.6733896,0.00421792,0.2729248,0.0101977,0.0005985798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08823986,0.003060727,0.8978387,0.002527058,0.0006105953,0.0008088214,0.001787919,0.001777071,0.003349225],"genre_scores_gemma":[0.8229048,0.0009292268,0.167554,0.002017807,0.0002968557,0.001648484,0.002399551,0.001097491,0.001151643],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2668743,"threshold_uncertainty_score":0.9040745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0342764187613661,"score_gpt":0.3272200656833215,"score_spread":0.2929436469219554,"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."}}