{"id":"W3090390366","doi":"10.1111/mec.15659","title":"Population genomics of parallel adaptation","year":2020,"lang":"en","type":"letter","venue":"Molecular Ecology","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biology; Parallel evolution; Adaptation (eye); Evolutionary biology; Ambrosia artemisiifolia; Population genomics; Natural selection; Selection (genetic algorithm); Population; Genomics; Molecular ecology; Ecology; Ragweed; Genome; Genetics; Phylogenetics; Artificial intelligence; Computer science; Gene","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":[0.001795658,0.0002695706,0.0003795754,0.0002330012,0.001378889,0.00152607,0.0007064591,0.008529619,0.003432855],"category_scores_gemma":[0.007632654,0.0001947134,0.0003135112,0.0002230396,0.002831539,0.002242972,0.001084357,0.009979763,0.002203023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001675928,"about_ca_system_score_gemma":0.0005247814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001254526,"about_ca_topic_score_gemma":0.001766028,"domain_scores_codex":[0.999096,0.0003517112,0.00003528607,0.0002024027,0.0002307899,0.00008381517],"domain_scores_gemma":[0.9960877,0.002693078,0.000195498,0.0003535602,0.0003820672,0.0002880788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002885295,0.00008341545,0.003905568,0.0002233186,0.00007243086,0.003110163,0.0006511974,0.001255484,0.01074181,0.1263353,0.6568594,0.1964735],"study_design_scores_gemma":[0.00009838096,0.0001131255,0.004384206,0.0001118658,0.00002242874,0.003252455,0.0004388169,0.002435223,0.00233883,0.2391125,0.7476255,0.00006668201],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.00892326,0.009351067,0.006099633,0.9388523,0.0138249,0.0000218316,0.0001019887,0.0002220794,0.02260295],"genre_scores_gemma":[0.20619,0.00994589,0.003715473,0.7237288,0.0338369,0.0001406442,0.0001425512,0.0001117615,0.02218802],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008529619,"threshold_uncertainty_score":0.01215982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02423086880659568,"score_gpt":0.2250143249846425,"score_spread":0.2007834561780469,"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."}}