{"id":"W2793674395","doi":"10.1371/journal.pgen.1007717","title":"Quantifying how constraints limit the diversity of viable routes to adaptation","year":2018,"lang":"en","type":"article","venue":"PLoS Genetics","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Calgary","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada; Hermon Slade Foundation; Genome Canada; Canadian Institutes of Health Research; Alberta Innovates; Alberta Innovates - Health Solutions","keywords":"Biology; Adaptation (eye); Pairwise comparison; Repeatability; Evolutionary biology; Trait; Local adaptation; Null model; Selection (genetic algorithm); Statistical hypothesis testing; Genome; Hypergeometric distribution; Genetic diversity; Genetics; Statistics; Gene; Ecology; Computer science; Machine learning; Mathematics","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":[0.00598947,0.0004810791,0.001278093,0.002007116,0.001046675,0.003378282,0.001120093,0.001377198,0.001549978],"category_scores_gemma":[0.04450981,0.0006297913,0.0007842425,0.002090129,0.002505505,0.005047071,0.002503809,0.001561894,0.0002104566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007100161,"about_ca_system_score_gemma":0.0006019558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001198579,"about_ca_topic_score_gemma":0.002011554,"domain_scores_codex":[0.9963171,0.001435166,0.0002797984,0.001115278,0.000561066,0.0002915259],"domain_scores_gemma":[0.9615157,0.0298561,0.003174557,0.003859718,0.0008160237,0.0007779628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005066533,0.0001812311,0.4670798,0.0005233071,0.001220491,0.0006604329,0.001652728,0.2431305,0.0618518,0.143021,0.0009737526,0.07919834],"study_design_scores_gemma":[0.00005343217,0.0002371161,0.2638464,0.0001052221,0.0002717453,0.0009654738,0.001401662,0.3421597,0.009784402,0.3772666,0.00367605,0.0002323576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9300423,0.0004253041,0.06564037,0.0002748236,0.00001149965,0.0000166597,0.0003540664,0.00009347772,0.003141466],"genre_scores_gemma":[0.9861174,0.0001724281,0.01301438,0.00006229347,0.00001642845,0.00004994549,0.0003723591,0.00005142808,0.0001432419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00598947,"threshold_uncertainty_score":0.03167576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05284433723732739,"score_gpt":0.2589887559768865,"score_spread":0.2061444187395591,"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."}}