{"id":"W4387536490","doi":"10.1101/2023.10.10.561651","title":"How to define, use, and interpret Pagel’s λ (lambda) in ecology and evolution","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Evolution and Paleontology Studies","field":"Earth and Planetary Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Phylogenetic tree; Trait; Imputation (statistics); Context (archaeology); Ecology; Measure (data warehouse); Evolutionary biology; Missing data; Statistics; Biology; Mathematics; Computer science; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004944345,0.0003476252,0.0004820341,0.0004456183,0.0001558332,0.0002250646,0.0002027238,0.0004359947,0.00001432373],"category_scores_gemma":[0.0006901752,0.0003510286,0.00004128012,0.0003766265,0.0002118108,0.000209182,0.0001984715,0.0005169359,0.0000506682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003558619,"about_ca_system_score_gemma":0.0001291149,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001129558,"about_ca_topic_score_gemma":0.0348081,"domain_scores_codex":[0.9980204,0.0002034743,0.0002949744,0.0008365855,0.0001592103,0.0004854152],"domain_scores_gemma":[0.9988722,0.0002963691,0.0001075788,0.0003699503,0.0001261095,0.0002277653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002968032,0.000009740668,0.9981476,0.000100584,0.00004450416,0.00004211418,0.00002565991,0.00004915807,0.0007365665,0.0002555009,0.0005556485,0.000003258055],"study_design_scores_gemma":[0.000226529,0.00007782835,0.9973171,0.0001309977,0.00002993559,5.828977e-8,0.0000176534,0.0007429951,0.00008535665,0.00001819389,0.0009827658,0.0003706373],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929997,0.002338465,0.0003137379,0.002321371,0.001089489,0.0004667469,0.0002725832,0.0001814018,0.00001655635],"genre_scores_gemma":[0.9969546,0.0007135222,0.001847856,0.0003069078,0.0001094778,0.00002894879,7.975e-7,0.00001528092,0.00002258602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03367854,"threshold_uncertainty_score":0.9998941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02528138473665043,"score_gpt":0.2104399447337315,"score_spread":0.1851585599970811,"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."}}