{"id":"W2134631886","doi":"10.1534/genetics.107.072926","title":"Adaptive Walks Toward a Moving Optimum","year":2007,"lang":"en","type":"article","venue":"Genetics","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"Emergent BioSolutions (Canada)","funders":"Natural Environment Research Council; Sight Research UK","keywords":"Biology; Adaptation (eye); Evolutionary biology; Fitness landscape; Genetics; Fixation (population genetics); Natural selection; Environmental change; Folding (DSP implementation); Phenotype; Evolutionary dynamics; Adaptive evolution; Stability (learning theory); Selection (genetic algorithm); Ecology; Gene; Climate change; Demography; Computer science; Neuroscience","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006460595,0.0004061044,0.0006151262,0.0007357186,0.0006885463,0.0009040594,0.0005825651,0.001004919,0.003746098],"category_scores_gemma":[0.002954066,0.0002950826,0.0008508985,0.0004023342,0.001106734,0.00129903,0.0007296656,0.0007895036,0.0003768915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006986574,"about_ca_system_score_gemma":0.0004737811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002821274,"about_ca_topic_score_gemma":0.002742479,"domain_scores_codex":[0.9998116,0.00007977747,0.000006539871,0.0000377347,0.00002306018,0.00004136764],"domain_scores_gemma":[0.999204,0.0003910769,0.00009952341,0.00008919962,0.00008854059,0.0001276763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002019623,0.00008418495,0.004859004,0.00006266783,0.0001006236,0.000253833,0.0002228529,0.9111807,0.006785285,0.06770048,0.001328766,0.007219785],"study_design_scores_gemma":[0.00006883037,0.0000955431,0.001495329,0.00001709772,0.00002601063,0.00005127362,0.0001037617,0.9518861,0.0006186566,0.04422949,0.001383743,0.00002428722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8755924,0.0002321236,0.1127144,0.0004949722,0.00006039457,0.00005080003,0.0001510797,0.0001948258,0.01050909],"genre_scores_gemma":[0.9611719,0.000187073,0.03422772,0.0001873853,0.00002279559,0.0001536701,0.0002347354,0.0000870879,0.003727618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003746098,"threshold_uncertainty_score":0.01253194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01252428216160527,"score_gpt":0.2570702233294987,"score_spread":0.2445459411678934,"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."}}