{"id":"W2914493757","doi":"10.1101/549329","title":"Unpacking conditional neutrality: genomic signatures of selection on conditionally beneficial and conditionally deleterious mutations","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":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Mount Royal University","funders":"Alberta Innovates - Health Solutions; Compute Canada","keywords":"Pleiotropy; Biology; Local adaptation; Genetics; Epistasis; Selection (genetic algorithm); Adaptation (eye); Conditional independence; Evolutionary biology; Background selection; Maladaptation; Neutral theory of molecular evolution; Allele; Gene; Computational biology; Phenotype; Population; Computer science; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001163957,0.0002495159,0.0004214963,0.0005303419,0.0002958192,0.0006029484,0.0005583956,0.0004901051,0.001731643],"category_scores_gemma":[0.00272971,0.0002154365,0.0004120283,0.0004598561,0.001006388,0.0007935155,0.0006318041,0.001010472,0.0001378564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003799247,"about_ca_system_score_gemma":0.0002521216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007669657,"about_ca_topic_score_gemma":0.0008649119,"domain_scores_codex":[0.9997,0.00009576957,0.00001750294,0.00008491853,0.00006438709,0.00003746479],"domain_scores_gemma":[0.9980843,0.001159413,0.0003092565,0.0002160445,0.0001011914,0.0001299021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000856915,0.0001765701,0.08005306,0.0002756688,0.0004046141,0.001062516,0.0003549017,0.10303,0.7423108,0.03019878,0.0005751121,0.04070115],"study_design_scores_gemma":[0.0001033724,0.0007776158,0.1757341,0.00005440338,0.0003215167,0.00171857,0.0004263741,0.6725781,0.09556255,0.05010312,0.002442968,0.000177341],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9690549,0.0001302864,0.02919571,0.00009734526,0.00001179089,0.00001086144,0.00009511365,0.0002591164,0.001144949],"genre_scores_gemma":[0.996574,0.00003502803,0.003141365,0.00004550315,0.000003599326,0.00000777972,0.00004571454,0.00003076057,0.000116322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001731643,"threshold_uncertainty_score":0.00615567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007634938353750244,"score_gpt":0.2253002823416658,"score_spread":0.2176653439879155,"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."}}