{"id":"W4388245906","doi":"10.1093/oso/9780198505235.003.0009","title":"Divergence along genetic lines of least resistance","year":2000,"lang":"en","type":"book-chapter","venue":"","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Divergence (linguistics); Adaptive radiation; Genetic divergence; Natural selection; Selection (genetic algorithm); Adaptive evolution; Evolutionary biology; Population; Adaptive strategies; Biology; Geography; Computer science; Sociology; Genetics; Artificial intelligence; Demography; Philosophy; Genetic diversity; Gene","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":[0.0002362705,0.0001497581,0.000189067,0.0005354232,0.0002139685,0.0007702436,0.0002962227,0.0003505584,0.00825298],"category_scores_gemma":[0.0008892035,0.00007781721,0.0001000048,0.0005534748,0.0008099974,0.0006695045,0.0002709812,0.000744595,0.001485453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004849046,"about_ca_system_score_gemma":0.0001297492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003054054,"about_ca_topic_score_gemma":0.0004874646,"domain_scores_codex":[0.9998259,0.00003839087,0.0000063585,0.00005334031,0.00006302605,0.00001308003],"domain_scores_gemma":[0.9996665,0.0001736405,0.00003607144,0.00002616681,0.00007234239,0.00002528984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001124092,0.00004288531,0.01098291,0.000227528,0.00004140871,0.0004224567,0.002542621,0.005473097,0.01464723,0.6496601,0.01220377,0.3036435],"study_design_scores_gemma":[0.00003019979,0.0001563333,0.03836098,0.0001925623,0.00002661843,0.001551027,0.0005514874,0.007270674,0.005999648,0.7482451,0.1975732,0.00004211874],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.2476018,0.005440271,0.06975677,0.00547834,0.0002875228,0.00003721313,0.0005894665,0.0003784177,0.6704302],"genre_scores_gemma":[0.8690856,0.001826267,0.0119141,0.0006623035,0.0001175756,0.00002488884,0.0004074611,0.0001192125,0.1158426],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.00825298,"threshold_uncertainty_score":0.02760899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0243055231967126,"score_gpt":0.2162429835423963,"score_spread":0.1919374603456837,"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."}}