{"id":"W2180001676","doi":"10.1111/j.0014-3820.2003.tb00326.x","title":"DIVERGENT SELECTION DRIVES THE ADAPTIVE RADIATION OF CROSSBILLS","year":2003,"lang":"en","type":"article","venue":"Evolution","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":215,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of British Columbia; Natural Sciences and Engineering Research Council of Canada; U.S. Forest Service; New Mexico State University; National Geographic Society; National Science Foundation","keywords":"Biology; Selection (genetic algorithm); Natural selection; Adaptive radiation; Adaptive evolution; Fitness landscape; Ecology; Population; Adaptive strategies; Evolutionary biology; Machine learning; Geography; Computer science; Demography","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":[0.0002200249,0.0001956786,0.0001736143,0.0004509855,0.000362147,0.0004820036,0.0002617081,0.0002286581,0.0009061841],"category_scores_gemma":[0.0004798271,0.0001255797,0.0001432117,0.0002588861,0.0006470439,0.0002489062,0.0007196111,0.0002952376,0.0001230176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000471328,"about_ca_system_score_gemma":0.00009537789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001586591,"about_ca_topic_score_gemma":0.004677452,"domain_scores_codex":[0.9998707,0.00002518558,0.000007245787,0.00005700226,0.00002518604,0.00001474795],"domain_scores_gemma":[0.999772,0.00005156008,0.00008159986,0.00003490809,0.0000311674,0.0000286757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001724232,0.00005031841,0.6003221,0.00004252229,0.0000941387,0.0001938931,0.0008350414,0.00238883,0.3831937,0.001497869,0.0001027747,0.01110648],"study_design_scores_gemma":[0.00000532901,0.00007746032,0.9917632,0.000005474171,0.00001510997,0.0002560374,0.0002749551,0.002242223,0.004482222,0.0005380335,0.0003306038,0.000009260002],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995037,0.00001841169,0.0001997342,0.000006764021,3.523949e-7,6.692696e-7,0.0000107338,0.000002129813,0.0002574946],"genre_scores_gemma":[0.9996291,0.00001054438,0.0002017033,0.00001190079,7.474597e-7,0.000001856399,0.0000397038,0.000002144065,0.0001022513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001586591,"threshold_uncertainty_score":0.003419697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01436168709814281,"score_gpt":0.2240512545237621,"score_spread":0.2096895674256193,"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."}}