{"id":"W2986340550","doi":"10.1093/jhered/esz065","title":"Predicting Adaptive Genetic Variation of Loblolly Pine (Pinus taeda L.) Populations Under Projected Future Climates Based on Multivariate Models","year":2019,"lang":"en","type":"article","venue":"Journal of Heredity","topic":"Forest ecology and management","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Institute of Food and Agriculture","keywords":"Loblolly pine; Pinus <genus>; Biology; Multivariate statistics; Genetic variation; Variation (astronomy); Multivariate analysis; Tree breeding; Woody plant; Ecology; Botany; Statistics; Genetics; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003547454,0.000103761,0.0001830825,0.00007071113,0.00007205212,0.00000892777,0.0001442234,0.00008902736,0.0005963343],"category_scores_gemma":[0.0000348085,0.00008323257,0.00007156804,0.0001602236,0.00003874817,0.0002545413,0.00005465173,0.0001890922,0.00002352267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001781722,"about_ca_system_score_gemma":0.00003284172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002022061,"about_ca_topic_score_gemma":0.0001744527,"domain_scores_codex":[0.9988945,0.0001062999,0.0003900619,0.0001394675,0.0003175908,0.0001520927],"domain_scores_gemma":[0.9991571,0.00005668551,0.000544486,0.0001458304,0.0000469817,0.00004893039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002387206,0.0003021609,0.06995734,0.00001758013,0.000041143,0.000006361463,0.000232852,0.9262413,0.001081922,0.001270461,0.0004264853,0.0001836509],"study_design_scores_gemma":[0.0006879655,0.0004381128,0.6743127,0.00002794774,0.00003751917,0.000003958377,0.00008280238,0.3196944,0.00004443512,0.004586395,0.00002498712,0.00005876147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9669872,0.00001389787,0.02958102,0.0003684363,0.0008970094,0.0003454033,0.000013683,0.00001314251,0.001780226],"genre_scores_gemma":[0.9880496,0.00000467484,0.01160259,0.00011337,0.0001553287,0.000003145597,0.00000409303,0.00000855527,0.00005870121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6065469,"threshold_uncertainty_score":0.6529442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01957812664916913,"score_gpt":0.2400863935051272,"score_spread":0.2205082668559581,"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."}}