{"id":"W4206706985","doi":"10.22541/au.164165840.02956352/v1","title":"Demographic history and conservation genomics of caribou (Rangifer tarandus) in Québec","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Gouvernement du Québec; Université Laval; Ministère des Ressources naturelles et des Forêts; Trent University","funders":"","keywords":"Endangered species; Effective population size; Coalescent theory; Biology; Threatened species; Evolutionary biology; Demographic history; Population; Genomics; Runs of Homozygosity; Population genomics; Linkage disequilibrium; Genetic diversity; Conservation genetics; Nucleotide diversity; Geography; Ecology; Demography; Genome; Genetics; Phylogenetics; Allele; Microsatellite; Single-nucleotide polymorphism; Haplotype; Genotype; Gene","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":[0.0003692843,0.0001316871,0.0001977964,0.0009331742,0.001348473,0.0006758258,0.0003821923,0.0002062028,0.001753673],"category_scores_gemma":[0.0006839402,0.0000687474,0.000126569,0.001685714,0.000454343,0.0002178712,0.0002585548,0.0002541126,0.0001190206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007280319,"about_ca_system_score_gemma":0.003331287,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9675865,"about_ca_topic_score_gemma":0.9860827,"domain_scores_codex":[0.9998533,0.00002396477,0.000004589325,0.00005370141,0.00002174742,0.00004266988],"domain_scores_gemma":[0.9994383,0.00009018396,0.00007459321,0.00002121637,0.0002820369,0.00009358976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001376301,0.00003887689,0.9586037,0.00004069498,0.00008704278,0.0002100132,0.00483637,0.00109219,0.008494874,0.0004892581,0.0009591046,0.02501022],"study_design_scores_gemma":[0.000001899038,0.00001115749,0.9974331,0.00001029106,0.000009056341,0.00003493534,0.0008729447,0.0005366222,0.00008081635,0.00002860043,0.0009743512,0.000006144552],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976705,0.0002515866,0.0002068576,0.00008165976,0.000001609899,0.000006907791,0.0008459361,0.000007226948,0.0009276363],"genre_scores_gemma":[0.9976069,0.0001567756,0.0003240261,0.00004820459,0.000001228053,0.000007690516,0.0007575742,0.000005095944,0.001092524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03241354,"threshold_uncertainty_score":0.06520879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01435710931577928,"score_gpt":0.2163922269537254,"score_spread":0.2020351176379462,"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."}}