{"id":"W3141331266","doi":"10.1111/jeb.13786","title":"Assortative mating can help adaptation of flowering time to a changing climate: Insights from a polygenic model","year":2021,"lang":"en","type":"article","venue":"Journal of Evolutionary Biology","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Montpellier Université d'Excellence; Centre National de la Recherche Scientifique; Agence Nationale de la Recherche","keywords":"Assortative mating; Biology; Mating; Population; Adaptation (eye); Selection (genetic algorithm); Genetic Fitness; Evolutionary biology; Local adaptation; Trait; Genetic model; Ecology; Genetics; Computer science; Biological evolution; Machine learning","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":[0.001013943,0.0004324544,0.0006717956,0.0005554164,0.0006781982,0.001105244,0.001254959,0.001043106,0.003571895],"category_scores_gemma":[0.002823386,0.0002825352,0.0009013909,0.0006409327,0.0008679384,0.001213302,0.0006744723,0.0007754464,0.00037275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007812056,"about_ca_system_score_gemma":0.0005376224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008135625,"about_ca_topic_score_gemma":0.00619449,"domain_scores_codex":[0.9997398,0.0001294485,0.0000102607,0.00005748999,0.0000242539,0.00003863621],"domain_scores_gemma":[0.9989886,0.0006022362,0.0001468428,0.00007879861,0.00005945672,0.0001240833],"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":[0.00008360602,0.0001157553,0.01497108,0.00005144514,0.0001471968,0.0004319172,0.0004121269,0.846734,0.003755143,0.1243338,0.001057124,0.007906945],"study_design_scores_gemma":[0.00002126922,0.00002641263,0.003229353,0.000006163478,0.00004069021,0.00007784752,0.00003925762,0.9606436,0.00007529075,0.03538737,0.000434064,0.00001866349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7635652,0.000653418,0.2201409,0.002008725,0.0000573426,0.00004637718,0.0003140359,0.0001828999,0.01303117],"genre_scores_gemma":[0.9861103,0.0004096901,0.009349026,0.0001554248,0.00003912735,0.00005455944,0.00008921176,0.00004327997,0.00374929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008135625,"threshold_uncertainty_score":0.01617652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03421664742389457,"score_gpt":0.2215964375647618,"score_spread":0.1873797901408672,"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."}}