{"id":"W2102531623","doi":"10.1139/f2012-092","title":"Temporal genetic variation as revealed by a microsatellite analysis of European sardine (<i>Sardina pilchardus</i>) archived samples","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministère de l’Agriculture, de l’Agroalimentaire et de la Forêt; Ministero delle Politiche Agricole Alimentari e Forestali; Centre National de la Recherche Scientifique; European Commission","keywords":"Sardine; Microsatellite; Genetic variation; Genetic variability; Genetic diversity; Population; Fishing; Biology; Stock (firearms); Population bottleneck; Fishery; Geography; Ecology; Allele; Demography; Genetics; Fish <Actinopterygii>","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0002601483,0.0001212459,0.0001059914,0.000806663,0.000146784,0.000216065,0.0001133662,0.0001665737,0.0003440321],"category_scores_gemma":[0.0004227641,0.00006959653,0.0001128933,0.0007718233,0.0001988577,0.00008372575,0.000188921,0.000102254,0.0001025521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000185767,"about_ca_system_score_gemma":0.0001130856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003085711,"about_ca_topic_score_gemma":0.007919154,"domain_scores_codex":[0.9998717,0.00001479256,0.00001522278,0.00005171439,0.0000291868,0.00001726285],"domain_scores_gemma":[0.9996408,0.00003549309,0.000166974,0.00003277231,0.00008142319,0.0000425822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001939392,0.00003733419,0.93409,0.00002221681,0.00005862982,0.0002781901,0.0007103829,0.0002614523,0.05475212,0.00003736453,0.00008093454,0.009477406],"study_design_scores_gemma":[0.000001551389,0.00003712727,0.9991467,0.00000121045,0.000005701351,0.0001126839,0.00006641928,0.00006844131,0.0003920332,0.000003911496,0.0001630708,0.000001185908],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996814,0.00002137973,0.00005930059,0.000002600809,6.211934e-7,0.000001433416,0.0001213742,0.000001404874,0.0001105458],"genre_scores_gemma":[0.9986866,0.00003256599,0.0002761201,0.000006085174,0.000002181581,0.000008994197,0.0007982123,9.945149e-7,0.0001882518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003085711,"threshold_uncertainty_score":0.006135464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01746572085073816,"score_gpt":0.2204200967742924,"score_spread":0.2029543759235542,"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."}}