{"id":"W2917816349","doi":"10.1002/tafs.10157","title":"Development and Application of Single‐Nucleotide Polymorphism (<scp>SNP</scp>) Genetic Markers for Conservation Monitoring of Burbot Populations","year":2019,"lang":"en","type":"article","venue":"Transactions of the American Fisheries Society","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fisheries and Oceans Canada; Bonneville Power Administration","keywords":"Biology; Population; Genetic diversity; Single-nucleotide polymorphism; SNP; Inbreeding; Population bottleneck; Fishery; Genetic monitoring; Ecology; Zoology; Genetics; Microsatellite; Allele; Demography; Genotype; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001386027,0.0003572942,0.0002539633,0.001337814,0.0002991334,0.0005930038,0.0005284924,0.0004050869,0.0006967667],"category_scores_gemma":[0.00102264,0.0002407489,0.0002926041,0.0008697005,0.0002326995,0.0002377586,0.0004311498,0.0005402092,0.000254036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003374688,"about_ca_system_score_gemma":0.0006060988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005450931,"about_ca_topic_score_gemma":0.01535068,"domain_scores_codex":[0.9993659,0.0001497624,0.00004407976,0.0002053277,0.000194708,0.00004029581],"domain_scores_gemma":[0.9990134,0.0002110539,0.0002899134,0.00006318087,0.0003218022,0.0001006278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000255771,0.0002716177,0.355569,0.0002104936,0.0001666069,0.0001834042,0.0003599005,0.003133246,0.5236564,0.0005167188,0.0009683676,0.1147084],"study_design_scores_gemma":[0.00008584345,0.0007683294,0.8542566,0.0001555514,0.0002570184,0.0005160475,0.0004780669,0.02699581,0.1070917,0.0005379783,0.008799948,0.00005696993],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.958051,0.0006978005,0.03524958,0.0001351902,0.00003328563,0.0002789263,0.002839681,0.0002124402,0.002502185],"genre_scores_gemma":[0.8531557,0.0003931385,0.1416142,0.0002320487,0.00001650415,0.0003499205,0.002675405,0.00002718379,0.001536083],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005450931,"threshold_uncertainty_score":0.01083839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01877369278380254,"score_gpt":0.2279616154862665,"score_spread":0.209187922702464,"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."}}