{"id":"W4200417959","doi":"10.1371/journal.pone.0261966","title":"Genotyping-in-Thousands by sequencing panel development and application to inform kokanee salmon (Oncorhynchus nerka) fisheries management at multiple scales","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Forests; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"University of British Columbia; Freshwater Fisheries Society of British Columbia","keywords":"Oncorhynchus; Ecotype; Fish migration; Genotyping; Fishery; Biology; Fisheries management; SNP genotyping; Microsatellite; Ecology; Genetics; Genotype; Fish <Actinopterygii>; Fishing; Gene; Allele","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002631304,0.0004773317,0.0004740402,0.0008968376,0.0005391193,0.0007535388,0.0005786477,0.0004712148,0.002329238],"category_scores_gemma":[0.002405626,0.0004940211,0.0004572078,0.0009497658,0.000251998,0.0003608975,0.0007452389,0.0006899199,0.001001672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006880485,"about_ca_system_score_gemma":0.001633634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03721243,"about_ca_topic_score_gemma":0.1971906,"domain_scores_codex":[0.9987711,0.0002421517,0.00006826633,0.0004444878,0.0003725774,0.0001014741],"domain_scores_gemma":[0.9984742,0.0002890153,0.0002567828,0.0002307457,0.0006134702,0.0001358203],"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":[0.000704444,0.0002011069,0.2191907,0.0005206807,0.0007702489,0.0002762976,0.001320525,0.01714865,0.5420446,0.001978443,0.01295444,0.2028899],"study_design_scores_gemma":[0.0001178242,0.0006270364,0.6697025,0.0001828415,0.000597442,0.0005379461,0.0007457918,0.0731555,0.1699991,0.002734454,0.08137987,0.0002196888],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6101705,0.001108982,0.3251658,0.0005658245,0.0002016419,0.001208975,0.04414458,0.004850312,0.01258341],"genre_scores_gemma":[0.4945525,0.000647118,0.4555921,0.001066387,0.00005051344,0.001416181,0.03610883,0.0006547266,0.009911704],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03721243,"threshold_uncertainty_score":0.07399166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02609861119042883,"score_gpt":0.1994443491228197,"score_spread":0.1733457379323909,"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."}}