{"id":"W2948102794","doi":"10.1101/660621","title":"Adaptive and maladaptive genetic diversity in small populations; insights from the Brook Charr ( <i>Salvelinus fontinalis)</i> case study","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère des Forêts, de la Faune et des Parcs; Université Laval","keywords":"Salvelinus; Biology; Fish migration; Local adaptation; Fontinalis; Evolutionary biology; Genetic drift; Ecology; Adaptation (eye); Genetic variation; Context (archaeology); Transposable element; Genetic diversity; Genetics; Gene; Population; Genome; Fish <Actinopterygii>; Fishery; Demography; Trout; Habitat","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":[0.0006237585,0.0002391425,0.0002382755,0.001205382,0.001042511,0.0005151847,0.0004515828,0.0002835085,0.0007732386],"category_scores_gemma":[0.0007812905,0.00006816653,0.0002218172,0.0009945935,0.0007333268,0.0001026415,0.0003219036,0.000266008,0.00007925661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00152191,"about_ca_system_score_gemma":0.0008892023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.239368,"about_ca_topic_score_gemma":0.4592262,"domain_scores_codex":[0.9997234,0.00006859787,0.00001059444,0.00009708639,0.00005764045,0.00004266197],"domain_scores_gemma":[0.9992921,0.0002053081,0.0001696591,0.00005732041,0.0001478295,0.000127786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000230318,0.00008670431,0.9500384,0.00008970546,0.0001816022,0.009508064,0.003732225,0.001040631,0.01655783,0.0004393037,0.0008430577,0.01725207],"study_design_scores_gemma":[0.00001144496,0.00009311436,0.9867532,0.0000299124,0.0000640349,0.004768831,0.002427265,0.001833719,0.001280835,0.000208629,0.002507512,0.00002149805],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989682,0.0001459859,0.0003308927,0.00004245893,0.000001252166,0.000006463865,0.0001239861,0.000005552949,0.0003751074],"genre_scores_gemma":[0.998557,0.0001121385,0.0007757158,0.00003130562,0.000003976577,0.000004276847,0.0001889684,0.000004769571,0.0003218748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.239368,"threshold_uncertainty_score":0.4759493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02470426759742399,"score_gpt":0.2232565179807392,"score_spread":0.1985522503833152,"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."}}