{"id":"W2238739307","doi":"10.1111/cobi.12674","title":"Role of genomics and transcriptomics in selection of reintroduction source populations","year":2016,"lang":"en","type":"article","venue":"Conservation Biology","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Genomics; Selection (genetic algorithm); Biology; Evolutionary biology; Computational biology; Population genomics; Genomic selection; Transcriptome; Genetics; Genome; Computer science; Gene; Genotype; Single-nucleotide polymorphism; Artificial intelligence; Gene expression","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":[0.00007639986,0.00004726428,0.00008292136,0.00006168416,0.00001891112,0.000001177856,0.00003612443,0.0001157337,0.00001472879],"category_scores_gemma":[0.00004860293,0.00004151891,0.00001985818,0.00006636456,0.00007651228,0.00000355587,0.00001464362,0.00001950192,3.130903e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006742546,"about_ca_system_score_gemma":0.0000237368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007439122,"about_ca_topic_score_gemma":0.0001722032,"domain_scores_codex":[0.9995779,0.00004524245,0.0001721659,0.0001257886,0.00002105433,0.00005788343],"domain_scores_gemma":[0.9997296,0.000007581251,0.00009628788,0.00007575238,0.00007728698,0.00001346941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00004381131,0.000005666519,0.3038846,0.000003496676,0.000005224845,3.811254e-9,0.00003962402,0.00004736658,0.6903677,0.001767652,0.00002831529,0.003806561],"study_design_scores_gemma":[0.0006263794,0.0001356607,0.6548188,0.000006931898,0.00001200983,0.000005396384,0.00008953147,0.0001730765,0.3246666,0.003070389,0.01631336,0.00008190904],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926575,0.0001199355,0.006552177,0.0004799376,0.00006035447,0.00007371329,0.00001816223,0.000002769889,0.0000354221],"genre_scores_gemma":[0.9983367,0.00009194179,0.001355393,0.000045094,0.00003343947,0.000001589334,0.00005478333,0.000003307237,0.00007769314],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3657011,"threshold_uncertainty_score":0.1693092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01365116294209435,"score_gpt":0.2257548625225185,"score_spread":0.2121036995804241,"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."}}