{"id":"W2029299811","doi":"10.1577/m06-253.1","title":"The Application of Microsatellites for Stock Identification of Yukon River Chinook Salmon","year":2008,"lang":"en","type":"article","venue":"North American Journal of Fisheries Management","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"U.S. Fish and Wildlife Service","keywords":"Microsatellite; Locus (genetics); Biology; Allele; Oncorhynchus; Population; Chinook wind; Stock (firearms); Genetics; SNP; Single-nucleotide polymorphism; Allele frequency; Fishery; Genotype; Geography; Demography; Gene; Fish <Actinopterygii>","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":[0.0009503568,0.0001250667,0.00009448118,0.0004987151,0.000271136,0.0002545313,0.0002262087,0.0001474105,0.0003354588],"category_scores_gemma":[0.001395072,0.0000954829,0.0001089665,0.0004125218,0.0001829729,0.0001840609,0.0003501928,0.0001221959,0.00007142439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006550268,"about_ca_system_score_gemma":0.001068476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02776424,"about_ca_topic_score_gemma":0.09377085,"domain_scores_codex":[0.9997054,0.00008661461,0.00002701644,0.0000637768,0.00009206308,0.00002505308],"domain_scores_gemma":[0.9992982,0.0001276167,0.0001380317,0.00005070464,0.0002963235,0.00008917393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001458049,0.00003258225,0.8958811,0.00004324878,0.00005463056,0.00007273444,0.0005093263,0.00174997,0.05240168,0.0001389679,0.0002023317,0.04876762],"study_design_scores_gemma":[0.00001859566,0.0002153301,0.9861013,0.00001759622,0.00003056585,0.00008820525,0.000687532,0.007081606,0.004797436,0.0001676175,0.0007837057,0.00001050538],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981172,0.00005211912,0.001451449,0.00002478326,0.000001487064,0.00001906106,0.0001315939,0.00001185345,0.0001904575],"genre_scores_gemma":[0.995012,0.00003282947,0.004604018,0.0000109871,8.495809e-7,0.00002532543,0.0002015917,0.000001505871,0.000110914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02776424,"threshold_uncertainty_score":0.05520523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00730831133530045,"score_gpt":0.2082367916763023,"score_spread":0.2009284803410019,"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."}}