{"id":"W2140821838","doi":"10.1111/j.1654-1103.2012.12036.x","title":"Selecting traits that explain species–environment relationships: a generalized linear mixed model approach","year":2012,"lang":"en","type":"article","venue":"Journal of Vegetation Science","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":201,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Higher Education Commission, Pakistan; Higher Education Commision, Pakistan; McGill University","keywords":"Trait; Generalized linear mixed model; Generalized linear model; Abundance (ecology); Ecology; Environmental niche modelling; Mixed model; Environmental data; Statistics; Heteroscedasticity; Biology; Ordination; Generalized additive model; Relative species abundance; Species distribution; Linear model; Mathematics; Ecological niche; Habitat; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033836,0.0001300506,0.0001884949,0.0001590626,0.0007053067,0.00003462658,0.0003095163,0.00006070228,0.00006503927],"category_scores_gemma":[0.0003509283,0.0001089638,0.00007807767,0.0004864432,0.0004965933,0.001658048,0.00009515482,0.0002956796,0.00006848456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003300234,"about_ca_system_score_gemma":0.00004982594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001208843,"about_ca_topic_score_gemma":0.000005510826,"domain_scores_codex":[0.9980638,0.000145509,0.0004482136,0.0001955925,0.0007778154,0.0003690408],"domain_scores_gemma":[0.9989639,0.0001478915,0.0005437611,0.0001080252,0.00004863703,0.0001878083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00002315743,0.0002683003,0.1950499,0.000009897578,0.00002270224,0.000001336093,0.01079293,0.7645963,0.02487323,0.003362263,0.0002158823,0.0007841585],"study_design_scores_gemma":[0.0005408261,0.00005930958,0.6029701,0.00001175833,0.00003785762,0.00007058959,0.00116347,0.3915055,0.001934018,0.001298915,0.0002117607,0.000195879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7933866,0.0001345226,0.1995734,0.0002272661,0.0001956087,0.000115861,6.143026e-7,0.00001091835,0.006355251],"genre_scores_gemma":[0.883625,0.00004405078,0.1157761,0.0001213231,0.00006955182,0.000007041361,7.927595e-7,0.000007404255,0.000348811],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4079203,"threshold_uncertainty_score":0.542472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07629656983422417,"score_gpt":0.2757862973713381,"score_spread":0.199489727537114,"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."}}