{"id":"W1557412824","doi":"10.1111/j.1558-5646.2011.01333.x","title":"ADAPTIVE LANDSCAPES IN EVOLVING POPULATIONS OF PSEUDOMONAS FLUORESCENS","year":2011,"lang":"en","type":"article","venue":"Evolution","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Ontario Genomics","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biology; Replicate; Xylose; Adaptation (eye); Experimental evolution; Selection (genetic algorithm); Pseudomonas fluorescens; Fitness landscape; Evolutionary biology; Adaptive evolution; Local adaptation; Genetics; Gene; Population; Statistics; Artificial intelligence; Computer science; Food science; Bacteria","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":[0.0005460056,0.0001637143,0.0003123974,0.0006137456,0.0003190436,0.0005516646,0.0002543586,0.0003145591,0.0002310707],"category_scores_gemma":[0.002872035,0.0001409954,0.000227146,0.0004023617,0.0005320857,0.0003558349,0.0004209597,0.000371737,0.0000427648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004934493,"about_ca_system_score_gemma":0.0001160014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009340749,"about_ca_topic_score_gemma":0.001020932,"domain_scores_codex":[0.9997013,0.0001035469,0.00001894253,0.00007935122,0.00005983515,0.0000369668],"domain_scores_gemma":[0.999082,0.0004350251,0.0001973432,0.0001127488,0.00009889763,0.00007405325],"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":[0.0005203359,0.0001699781,0.1133847,0.0001131609,0.0002824723,0.0004781645,0.0008987594,0.04869678,0.8006504,0.002165606,0.0001274957,0.03251209],"study_design_scores_gemma":[0.00007010652,0.001100924,0.7627242,0.00002315096,0.0001061304,0.001104533,0.0008113617,0.1773798,0.04735732,0.007830752,0.001327842,0.0001639407],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984913,0.00002303994,0.001346263,0.00001047527,5.428686e-7,0.000003783686,0.00001865946,0.00001033017,0.0000956017],"genre_scores_gemma":[0.9981011,0.00002000085,0.001703001,0.0000110226,9.636752e-7,0.00001273521,0.00008585323,0.000005385097,0.00005997215],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009340749,"threshold_uncertainty_score":0.003580272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02249367836602848,"score_gpt":0.2455904031193468,"score_spread":0.2230967247533183,"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."}}