{"id":"W2991209293","doi":"10.2135/cropsci2019.05.0353","title":"Using Living Germplasm Collections to Characterize, Improve, and Conserve Woody Perennials","year":2019,"lang":"en","type":"article","venue":"Crop Science","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Dalhousie University","funders":"","keywords":"Germplasm; Perennial plant; Biology; Woody plant; Agroforestry; Biodiversity; Genetic diversity; Ex situ conservation; Agronomy; Botany; Ecology; Habitat; Endangered species","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":[0.00454661,0.0004723122,0.0004828235,0.003246289,0.0008616876,0.00161961,0.001211061,0.0004699985,0.002003522],"category_scores_gemma":[0.002880399,0.0002414482,0.000438529,0.002917412,0.0005930897,0.001615337,0.000997007,0.0008452422,0.001035885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003865869,"about_ca_system_score_gemma":0.0006791095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001264986,"about_ca_topic_score_gemma":0.004196623,"domain_scores_codex":[0.9988267,0.0003393677,0.0001628977,0.0003205833,0.0002907936,0.00005966336],"domain_scores_gemma":[0.9963785,0.0009437219,0.0009066894,0.0006483523,0.0008837762,0.0002388597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002007161,0.0002389601,0.07089914,0.003328337,0.0005387078,0.001159988,0.002310716,0.001505823,0.2462689,0.004330392,0.008077512,0.6611409],"study_design_scores_gemma":[0.0001248561,0.0006653753,0.4824553,0.002228117,0.001078083,0.003691196,0.003100922,0.001558836,0.07327672,0.007210375,0.4243631,0.0002470899],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6386213,0.07650774,0.2220776,0.00213014,0.00120808,0.001649749,0.02460777,0.001901996,0.03129559],"genre_scores_gemma":[0.5444794,0.03767332,0.3593095,0.001479251,0.0004357561,0.0008399722,0.04513615,0.0008183504,0.009828323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00454661,"threshold_uncertainty_score":0.02404505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07380254724441274,"score_gpt":0.2509493318292711,"score_spread":0.1771467845848584,"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."}}