{"id":"W2029224266","doi":"10.1007/s11295-009-0211-3","title":"Optimization of combined genetic gain and diversity for collection and deployment of seed orchard crops","year":2009,"lang":"en","type":"article","venue":"Tree Genetics & Genomes","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Seed orchard; Biology; Genetic gain; Genetic diversity; Selection (genetic algorithm); Pollination; Population; Biotechnology; Agronomy; Pollen; Genetic variation; Botany; Genetics; Computer science; Demography","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":[0.002122547,0.0009815202,0.001894059,0.002602113,0.001263974,0.001847797,0.001650433,0.001520911,0.003267718],"category_scores_gemma":[0.004073924,0.0009524444,0.001336931,0.001705941,0.0006116836,0.001494554,0.002704443,0.002790531,0.001267745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001685931,"about_ca_system_score_gemma":0.00168637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002197803,"about_ca_topic_score_gemma":0.01061249,"domain_scores_codex":[0.9978629,0.0005460893,0.0001895828,0.0007267856,0.0003265781,0.0003480755],"domain_scores_gemma":[0.9970598,0.001106974,0.000478791,0.0003850637,0.0003789198,0.0005905104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005061566,0.0005717184,0.004998515,0.000146887,0.000115712,0.0001342738,0.0002006252,0.004188697,0.9621325,0.0006351118,0.0002031445,0.02616674],"study_design_scores_gemma":[0.0007559144,0.001898253,0.05768685,0.0001114831,0.001008883,0.000901864,0.0006410457,0.02708967,0.8977045,0.001224826,0.01075509,0.000221455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9254231,0.0005633194,0.06650894,0.0003552116,0.00004727923,0.001226244,0.001569921,0.001076528,0.003229371],"genre_scores_gemma":[0.8336355,0.0006157564,0.1554854,0.0004190733,0.0000621634,0.001185546,0.004589363,0.0006824745,0.00332479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003267718,"threshold_uncertainty_score":0.01223236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03093945317955549,"score_gpt":0.1996022522653324,"score_spread":0.1686627990857769,"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."}}