{"id":"W2168900128","doi":"10.1098/rspb.2012.0466","title":"Speciation with gene flow in a heterogeneous virtual world: can physical obstacles accelerate speciation?","year":2012,"lang":"en","type":"article","venue":"Proceedings of the Royal Society B Biological Sciences","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Genetic algorithm; Gene flow; Flow (mathematics); Evolutionary biology; Biology; Computer science; Gene; Genetics; Physics; Mechanics; Genetic variation","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003181811,0.000125741,0.0001327064,0.00001583292,0.0001574302,0.00003141464,0.0003225296,0.00008351709,0.00001249803],"category_scores_gemma":[0.00005480563,0.00007034031,0.0001164802,0.0003469441,0.0004144258,0.000008290739,0.0001613268,0.00008543924,0.000001564146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003708283,"about_ca_system_score_gemma":0.0000308869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001900089,"about_ca_topic_score_gemma":0.00003384541,"domain_scores_codex":[0.9990378,0.00001508354,0.0001564689,0.0002587427,0.0002156714,0.00031624],"domain_scores_gemma":[0.999656,0.00001398803,0.0001356567,0.00005581916,0.00007336417,0.00006515139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005106245,0.0002710278,0.8503656,0.000007291364,0.0000315762,5.400698e-8,0.0004968045,0.003839398,0.1424707,0.001038628,0.0004652622,0.0009626026],"study_design_scores_gemma":[0.0007110509,0.001001277,0.8045613,0.00002682446,0.00002430613,0.000005998928,0.000787008,0.03122289,0.1591361,0.0006583666,0.001370362,0.0004945158],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984856,0.0001095034,0.00007898645,0.000269098,0.00006499646,0.0001456986,0.00001086511,0.000008614707,0.0008266555],"genre_scores_gemma":[0.9973851,0.00004341417,0.001773879,0.0002189053,0.0003115787,0.00001115117,0.000007107362,0.000004535004,0.0002443277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04580423,"threshold_uncertainty_score":0.2868394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0148317446825492,"score_gpt":0.2402808166131121,"score_spread":0.2254490719305629,"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."}}