{"id":"W2066667259","doi":"10.1111/j.1752-4571.2012.00280.x","title":"Understanding admixture patterns in supplemented populations: a case study combining molecular analyses and temporally explicit simulations in <scp>A</scp>tlantic salmon","year":2012,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Région Normandie; Office National de l’Eau et des Milieux Aquatiques; Institut National de la Recherche Agronomique","keywords":"Stocking; Biology; Biological dispersal; Hatchery; Microsatellite; Endangered species; Fish <Actinopterygii>; Ecology; Fishery; Population; Habitat; Allele; Gene; Genetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002187849,0.000148198,0.0001575632,0.0002192329,0.0003985334,0.00001513881,0.00009750234,0.00005459812,0.0001144976],"category_scores_gemma":[0.00005596749,0.000162663,0.00002382699,0.0006888036,0.0000633093,0.0003754416,0.0002268013,0.0001430123,0.00001860519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002913307,"about_ca_system_score_gemma":0.00000668701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001003136,"about_ca_topic_score_gemma":0.01304608,"domain_scores_codex":[0.9987869,0.0001159507,0.0003273258,0.0003060001,0.0001534307,0.0003104083],"domain_scores_gemma":[0.9993878,0.0002250797,0.00009328849,0.0002173795,0.000007039484,0.00006940013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000001508548,0.0006055027,0.9875417,0.000007317212,0.0000214946,0.00004172436,0.001308794,0.007166924,0.00008304905,0.002294098,0.0009194828,0.000008419374],"study_design_scores_gemma":[0.0005213665,0.00003584423,0.9736723,0.000009157445,0.00004924923,0.00003627209,0.01796078,0.005193051,0.0000025617,0.002186465,0.0002441682,0.0000887313],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765478,0.00009971455,0.02100253,0.0002942304,0.00002500422,0.001304057,0.00002940286,0.00004205834,0.0006551566],"genre_scores_gemma":[0.9980057,0.00001522264,0.001057116,0.0001174002,0.00001435899,0.0006056225,0.0001204513,0.00001192031,0.00005217231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02145789,"threshold_uncertainty_score":0.728002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1031086451344771,"score_gpt":0.3352663971961864,"score_spread":0.2321577520617094,"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."}}