{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009806813,0.00007374151,0.00008043688,0.00007311042,0.00002937964,0.000001914859,0.00007658284,0.00009239175,0.00003417575],"category_scores_gemma":[0.00005076152,0.00007748936,0.00004682644,0.0001133367,0.00003686505,0.000004221375,0.0000395011,0.00004718205,0.000007490215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002475862,"about_ca_system_score_gemma":0.00004106813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002102496,"about_ca_topic_score_gemma":0.001340951,"domain_scores_codex":[0.9994183,0.00004068417,0.0001778897,0.0001569597,0.0000748997,0.0001312599],"domain_scores_gemma":[0.9996651,0.000002702165,0.00006390548,0.0001626991,0.00007350579,0.00003205415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002661224,0.0004463565,0.6402337,0.0000281797,0.00004896793,0.00000291693,0.0008340342,0.003472293,0.3267747,0.02460637,0.00229726,0.0009891497],"study_design_scores_gemma":[0.0004637465,0.0001894658,0.9738955,0.00002020878,0.00001146448,0.000005932202,0.0002273069,0.01721833,0.005486919,0.002222168,0.0001143236,0.0001446101],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9538425,0.0007856003,0.03847111,0.00001945205,0.000146918,0.0001412129,0.000016982,0.00001262791,0.006563617],"genre_scores_gemma":[0.9920986,0.00002304834,0.007568157,0.00001054565,0.00004319929,0.000006863582,0.00005188214,0.000008463505,0.0001892524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3336618,"threshold_uncertainty_score":0.3159925,"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."}}