{"id":"W3092631867","doi":"10.1101/2020.10.09.330191","title":"Phenotypic and molecular evolution across 10,000 generations in laboratory budding yeast populations","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Department of Biotechnology, Ministry of Science and Technology, India; National Defense Science and Engineering Graduate; National Science Foundation; National Institutes of Health; Harvard University","keywords":"Biology; Ploidy; Adaptation (eye); Experimental evolution; Fixation (population genetics); Loss of heterozygosity; Evolutionary biology; Evolutionary dynamics; Phenotype; Genetics; Adaptability; Selection (genetic algorithm); Allele; Ecology; Population; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007178225,0.0001301801,0.0001674583,0.0002178792,0.0002705767,0.0003321223,0.0002839413,0.0002202384,0.0004238941],"category_scores_gemma":[0.001204049,0.00008359762,0.0002214504,0.0002211784,0.0002644112,0.0001830996,0.0002655478,0.0005140829,0.00007661634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006593328,"about_ca_system_score_gemma":0.0002039498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001238587,"about_ca_topic_score_gemma":0.001690208,"domain_scores_codex":[0.9997893,0.00006417044,0.00001871035,0.00005806733,0.00004863854,0.00002115055],"domain_scores_gemma":[0.9993893,0.0002826343,0.00008978282,0.0001068144,0.00006350474,0.00006796055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005694189,0.0004316269,0.09038259,0.00009864197,0.0002294297,0.0002787659,0.0003468795,0.02088722,0.8578211,0.001623281,0.000398562,0.02693248],"study_design_scores_gemma":[0.0001331569,0.002585609,0.5229771,0.00003735716,0.0002210918,0.0007551481,0.0007940147,0.119389,0.3452758,0.002320002,0.005372833,0.000138931],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990627,0.00005057877,0.0006819222,0.00001205855,0.000002766316,0.00000344919,0.00005956079,0.000009182609,0.0001177864],"genre_scores_gemma":[0.9975406,0.00005274173,0.001760961,0.00002262452,0.000001989907,0.00001910999,0.0003904219,0.000009209631,0.0002022434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001238587,"threshold_uncertainty_score":0.004783809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01329963997108996,"score_gpt":0.2459375944814808,"score_spread":0.2326379545103908,"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."}}