{"id":"W2613267362","doi":"10.1093/jhered/esx042","title":"Effects of Gene Action, Marker Density, and Timing of Selection on the Performance of Landscape Genomic Scans of Local Adaptation","year":2017,"lang":"en","type":"article","venue":"Journal of Heredity","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of Minnesota; National Science Foundation","keywords":"Locus (genetics); Biology; False positive paradox; Local adaptation; Genetics; Background selection; Adaptation (eye); Pleiotropy; Genotype; Gene; Selection (genetic algorithm); Genome Scan; Evolutionary biology; Computational biology; Allele; Statistics; Phenotype; Computer science; Population; Artificial intelligence; Mathematics","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":[0.0002242289,0.0000526433,0.0001342201,0.00002187702,0.00004870678,0.000002740639,0.00009997639,0.00006049661,0.000005856369],"category_scores_gemma":[0.00005050192,0.00003791599,0.00004601592,0.00001520049,0.0001107147,0.000005355848,0.00002328881,0.00006672469,6.426338e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004802732,"about_ca_system_score_gemma":0.00006144067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001645572,"about_ca_topic_score_gemma":0.0000144257,"domain_scores_codex":[0.9995309,0.00004291938,0.000214457,0.00005566045,0.0001062438,0.0000498511],"domain_scores_gemma":[0.9990733,0.00003152932,0.0005906668,0.0001065129,0.0001783634,0.00001960786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0006136379,0.00008850342,0.04638924,0.0002071128,0.0001160041,1.677076e-7,0.0001342125,0.004230107,0.9414532,0.00003500282,0.0002216037,0.006511219],"study_design_scores_gemma":[0.0002378219,0.0005670685,0.5232071,0.00003401177,0.00003144884,0.00001157122,0.00004768265,0.0004307484,0.4753694,0.00003392474,0.000009456388,0.00001984481],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931812,0.000195915,0.006323091,0.00002017245,0.000149774,0.000056111,0.000002598633,3.197915e-7,0.00007079125],"genre_scores_gemma":[0.9980594,0.0001077261,0.001682434,0.000004914437,0.0001244825,3.907665e-7,7.983558e-7,0.000003919517,0.00001591877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4768178,"threshold_uncertainty_score":0.1546169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01479891567034304,"score_gpt":0.2380503782051304,"score_spread":0.2232514625347874,"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."}}