{"id":"W1974507358","doi":"10.1016/j.tree.2011.02.008","title":"Adaptation in the age of ecological genomics: insights from parallelism and convergence","year":2011,"lang":"en","type":"review","venue":"Trends in Ecology & Evolution","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":433,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft","keywords":"Adaptation (eye); Parallel evolution; Biology; Natural selection; Evolutionary biology; Selection (genetic algorithm); Diversification (marketing strategy); Genomics; Phenotype; Phenotypic trait; Computational biology; Genome; Genetics; Computer science; Gene; Phylogenetics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001467222,0.000900007,0.001953318,0.00154372,0.0004057965,0.002083526,0.001576681,0.002231534,0.00221115],"category_scores_gemma":[0.001948039,0.0003122286,0.0004041631,0.00277318,0.002810442,0.004632435,0.001521346,0.00293168,0.0009038248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001229562,"about_ca_system_score_gemma":0.001431821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001234921,"about_ca_topic_score_gemma":0.001963522,"domain_scores_codex":[0.9996716,0.00007466422,0.00003055165,0.00009047623,0.0001026353,0.00003017124],"domain_scores_gemma":[0.9989319,0.000653834,0.0001039156,0.0000455842,0.0001716613,0.00009293848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006817199,0.0000294341,0.000570918,0.005285524,0.0001097841,0.0001852847,0.0001928669,0.0007028119,0.001248482,0.02601358,0.01696424,0.9486288],"study_design_scores_gemma":[0.00002204714,0.00006164766,0.003164827,0.002924959,0.0001274769,0.001320113,0.0003924299,0.000410844,0.0005298018,0.06414124,0.9268412,0.00006343393],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002553192,0.9956409,0.000935123,0.001882319,0.000315022,0.000002112502,0.0000112459,0.00001168599,0.0009461847],"genre_scores_gemma":[0.002549615,0.9952692,0.0005206456,0.000680299,0.0005722148,0.000005453918,0.00001579949,0.000003244893,0.0003835445],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002231534,"threshold_uncertainty_score":0.008921206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05027313339933455,"score_gpt":0.2821924361855702,"score_spread":0.2319193027862356,"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."}}