{"id":"W4210371918","doi":"10.1111/eva.13348","title":"Breeding for adaptation to climate change: genomic selection for drought response in a white spruce multi‐site polycross test","year":2022,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Forest ecology and management","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts; Natural Resources Canada; Canadian Forest Service; Université Laval","funders":"Génome Québec; Genome Canada","keywords":"Biology; Adaptation (eye); White (mutation); Selection (genetic algorithm); Climate change; Test (biology); Local adaptation; Drought tolerance; Ecology; Agronomy; Genetics; Gene; Demography","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":[0.0008908432,0.000641373,0.0002917186,0.0003661334,0.0003417362,0.0003403904,0.000420937,0.0002332212,0.001074182],"category_scores_gemma":[0.0004457503,0.0001248662,0.0003536671,0.0002307275,0.0002873939,0.0001027106,0.0003393087,0.0004873363,0.000142608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001820103,"about_ca_system_score_gemma":0.0002396763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001616254,"about_ca_topic_score_gemma":0.004351621,"domain_scores_codex":[0.9996524,0.0001310103,0.0000194054,0.0001192275,0.00004417898,0.00003376441],"domain_scores_gemma":[0.9991701,0.0003604271,0.0001297578,0.00008709115,0.00005902331,0.0001936005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001779979,0.001775904,0.1431705,0.000043701,0.0004405024,0.0008444187,0.0002111646,0.004562146,0.8330312,0.0004177759,0.0001552568,0.01356739],"study_design_scores_gemma":[0.0002334388,0.006034741,0.8726749,0.00001150209,0.0003735631,0.000919467,0.0002477914,0.02751707,0.09035102,0.0002482927,0.001334808,0.00005346002],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982071,0.000008172453,0.001486105,0.000005760719,0.000001811213,0.00001357744,0.00007887277,0.00001902972,0.0001796089],"genre_scores_gemma":[0.9954888,0.00001298644,0.003521656,0.00003414071,0.000003025921,0.00004185054,0.0004089787,0.00002154596,0.0004670763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001616254,"threshold_uncertainty_score":0.00471127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01964903516527203,"score_gpt":0.2629867847381693,"score_spread":0.2433377495728973,"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."}}