{"id":"W2161560292","doi":"10.1007/s11295-009-0255-4","title":"Managing genetic gain and diversity in clonal deployment of white spruce in New Brunswick, Canada","year":2009,"lang":"en","type":"article","venue":"Tree Genetics & Genomes","topic":"Forest ecology and management","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Forest Service; University of New Brunswick; Government of New Brunswick; Natural Resources Canada","funders":"","keywords":"Genetic gain; Selection (genetic algorithm); Biology; Index selection; Clonal selection; Genetic diversity; Trait; Tree breeding; Genetic model; Population; Genetics; Statistics; Demography; Genetic variation; Ecology; Mathematics; Computer science; Gene; Machine learning; Woody plant; Immunology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002122672,0.0002481047,0.0003383487,0.001085167,0.00379019,0.002011349,0.002408538,0.0004234801,0.0009829637],"category_scores_gemma":[0.002970689,0.0002083109,0.0001683048,0.001090078,0.0008508335,0.0004500605,0.000986314,0.0005587073,0.0001244928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04626433,"about_ca_system_score_gemma":0.03947667,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9839753,"about_ca_topic_score_gemma":0.9973215,"domain_scores_codex":[0.9990733,0.0001331401,0.00002538925,0.0001498774,0.0002295225,0.0003887731],"domain_scores_gemma":[0.9972298,0.0004730811,0.0002168355,0.0001084068,0.001204458,0.000767446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00123036,0.0008415655,0.6313717,0.0001147872,0.0003003417,0.001646008,0.007137497,0.05543395,0.05474851,0.00672969,0.009127692,0.2313179],"study_design_scores_gemma":[0.0003078967,0.0005705014,0.8465272,0.0001101013,0.0003409709,0.0004409174,0.02616763,0.07164421,0.01715169,0.002997634,0.03356079,0.000180549],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993318,0.0002306044,0.0009686388,0.0004102906,0.00001068175,0.00008704768,0.0001482245,0.00002438538,0.004802277],"genre_scores_gemma":[0.9929993,0.0001653384,0.002683203,0.0001429164,0.000003183472,0.00002820071,0.0001369415,0.00001189863,0.003829003],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04626433,"threshold_uncertainty_score":0.3356727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008094701654073659,"score_gpt":0.1909112402100243,"score_spread":0.1828165385559506,"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."}}