{"id":"W2338536601","doi":"10.1101/038695","title":"An Extended Maximum Likelihood Inference of Geographic Range Evolution by Dispersal, Local Extinction and Cladogenesis","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Evolution and Paleontology Studies","field":"Earth and Planetary Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo; National Aeronautics and Space Administration; Institut National de la Recherche Agronomique; Agence Nationale de la Recherche; National Science Foundation","keywords":"Cladogenesis; Biological dispersal; Inference; Range (aeronautics); Extinction (optical mineralogy); Maximum likelihood; Statistical physics; Evolutionary biology; Biology; Ecology; Geography; Mathematics; Physics; Statistics; Computer science; Paleontology; Artificial intelligence; Demography; Sociology; Phylogenetics; Engineering; Genetics","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":[0.003861136,0.0004681745,0.0007126389,0.00111978,0.0005268965,0.001371562,0.002330732,0.001150527,0.001809637],"category_scores_gemma":[0.01368004,0.0006819493,0.0007786805,0.001084197,0.001004849,0.001727257,0.001556235,0.00191344,0.0004111322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00086708,"about_ca_system_score_gemma":0.000702149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004483827,"about_ca_topic_score_gemma":0.004813242,"domain_scores_codex":[0.9989059,0.0006189846,0.00005071872,0.0002746133,0.00009246961,0.00005726476],"domain_scores_gemma":[0.990922,0.007115201,0.0005050692,0.0007906962,0.0004085285,0.0002584236],"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":[0.00009926171,0.00004791485,0.0147562,0.00005389873,0.00006647625,0.0001510545,0.00009952921,0.954669,0.001073834,0.01020733,0.001007899,0.0177676],"study_design_scores_gemma":[0.00000729767,0.00000454701,0.0007345584,0.000004180023,0.000002730289,0.00001885145,0.000005384112,0.9941606,0.00009578598,0.00482224,0.0001396913,0.000004094396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2140477,0.0003670809,0.7821142,0.0005652768,0.00003290379,0.00003731939,0.0005447945,0.0008162051,0.001474522],"genre_scores_gemma":[0.8150773,0.0001446927,0.1820757,0.0001819069,0.00007546956,0.0000834169,0.0009471604,0.0001682553,0.001246096],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004483827,"threshold_uncertainty_score":0.02041984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01006989345462029,"score_gpt":0.2101191592001382,"score_spread":0.2000492657455179,"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."}}