{"id":"W3006403960","doi":"10.3389/fgene.2020.00028","title":"In Situ Genetic Evaluation of European Larch Across Climatic Regions Using Marker-Based Pedigree Reconstruction","year":2020,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Forest ecology and management","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Reforestation; Tree breeding; Larch; Forest management; Range (aeronautics); Biodiversity; Productivity; Climate change; Agroforestry; Ecology; Environmental resource management; Afforestation; Adaptation (eye); Geography; Environmental science; Biology; Woody plant","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":[0.0007245631,0.00008565524,0.0001239709,0.0000507255,0.00004471117,0.000007298579,0.0001688978,0.00004868134,0.0001463069],"category_scores_gemma":[0.00006068879,0.00009814158,0.00002840734,0.0002674127,0.0001925675,0.00005587326,0.0001183557,0.00009885697,0.00002486169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001860109,"about_ca_system_score_gemma":0.00002025973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002356909,"about_ca_topic_score_gemma":0.0003654599,"domain_scores_codex":[0.9987025,0.0003138194,0.0002976544,0.0002314383,0.000244528,0.0002100101],"domain_scores_gemma":[0.999653,0.00001249531,0.000104906,0.000174023,0.00001065155,0.00004488129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002938626,0.00005465413,0.7365462,0.00003422589,0.000007346847,0.000009141995,0.0008323231,0.2466658,0.001271411,0.000007258604,0.001234929,0.01330733],"study_design_scores_gemma":[0.0006928573,0.00006015834,0.6179731,0.00001534013,0.00003013069,0.000001701919,0.0003935777,0.3790314,0.0002721698,0.001192656,0.0002396157,0.00009728689],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9704338,0.0001175765,0.02635775,0.0002329768,0.000345179,0.0003724942,0.000002564224,0.000007093032,0.002130521],"genre_scores_gemma":[0.9421666,0.00004458194,0.05755588,0.0001746485,0.00002312524,0.000007953735,0.000003342112,0.00001082805,0.0000129941],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1323656,"threshold_uncertainty_score":0.4002098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03948349163866686,"score_gpt":0.2782316013532358,"score_spread":0.2387481097145689,"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."}}