{"id":"W2626120835","doi":"10.1093/database/bax048","title":"PigVar: a database of pig variations and positive selection signatures","year":2017,"lang":"en","type":"article","venue":"Database","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Selection (genetic algorithm); Database; Computer science; Information retrieval; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001511095,0.001182639,0.001716951,0.005170065,0.0005246826,0.001419721,0.001808181,0.001354828,0.01788862],"category_scores_gemma":[0.002847794,0.0006266808,0.001255077,0.005744405,0.0004018678,0.0009079878,0.00153964,0.001009146,0.008701878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003682051,"about_ca_system_score_gemma":0.0009452493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001479037,"about_ca_topic_score_gemma":0.002135455,"domain_scores_codex":[0.9990753,0.0001517214,0.0001538023,0.000324694,0.0002033927,0.0000911231],"domain_scores_gemma":[0.9986563,0.0004349905,0.0003692958,0.0002627029,0.0001122068,0.0001644716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.005580118,0.0005120948,0.1090107,0.007377133,0.00205348,0.004413277,0.001090351,0.006104144,0.09159541,0.008711161,0.3864815,0.3770706],"study_design_scores_gemma":[0.0007994769,0.0006241183,0.1619692,0.0006884197,0.0009489194,0.005554378,0.0001790588,0.006013894,0.01109331,0.008029027,0.8038398,0.0002603507],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.06573433,0.003070363,0.03687478,0.0002613891,0.0002702062,0.0002305176,0.866275,0.01851059,0.008772944],"genre_scores_gemma":[0.03609738,0.0007941092,0.0328683,0.0002345818,0.0000875018,0.0003843758,0.925575,0.00217978,0.001778988],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01788862,"threshold_uncertainty_score":0.05984342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01137428249328636,"score_gpt":0.2650252092062056,"score_spread":0.2536509267129193,"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."}}