{"id":"W2078007789","doi":"10.1038/nature01151","title":"Macroevolution simulated with autonomously replicating computer programs","year":2002,"lang":"en","type":"article","venue":"Nature","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":88,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Oregon State University; M.J. Murdock Charitable Trust; National Science Foundation","keywords":"Macroevolution; Term (time); Adaptation (eye); Natural selection; Population; Selection (genetic algorithm); Set (abstract data type); Simple (philosophy); Computer science; Variation (astronomy); Process (computing); Type (biology); Outcome (game theory); Evolutionary biology; Mathematical economics; Biology; Artificial intelligence; Ecology; Mathematics; Genetics; Phylogenetic tree; Epistemology; Sociology","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":[0.00005786058,0.000112032,0.00007496718,0.00001889382,0.00007205953,0.00002140026,0.0001155408,0.0006299706,0.00002167678],"category_scores_gemma":[0.0000134457,0.0000930774,0.0000409301,0.000108213,0.00004654542,0.000001957882,0.00004376099,0.0004291996,0.00001838798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001552634,"about_ca_system_score_gemma":0.00001285965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003947297,"about_ca_topic_score_gemma":0.00002507847,"domain_scores_codex":[0.9992858,0.00002328897,0.0001078767,0.000298084,0.0001022893,0.0001826503],"domain_scores_gemma":[0.9994717,0.000002998309,0.00006027467,0.0003115856,0.00009738745,0.00005600822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004877796,0.001789176,0.1462235,0.000219627,0.0007611352,0.00006470845,0.0007567909,0.08474439,0.1848837,0.003493731,0.1274744,0.449101],"study_design_scores_gemma":[0.001898502,0.001268451,0.01938397,0.00004760011,0.00004310666,0.0001862356,0.00003563622,0.5983868,0.003853232,0.00007638989,0.374107,0.0007130301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9791983,0.00175551,0.01398324,0.0007799176,0.0001527027,0.000361149,0.000008450653,0.0001128117,0.003647904],"genre_scores_gemma":[0.9877799,0.00002847579,0.009129701,0.0007799547,0.0001882168,0.000005240051,0.0001797753,0.00001807023,0.001890666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5136425,"threshold_uncertainty_score":0.485891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005089882663750327,"score_gpt":0.2205360835521474,"score_spread":0.2154462008883971,"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."}}