{"id":"W3012448306","doi":"10.3389/fgene.2020.00216","title":"Exploring Phenotypes for Disease Resilience in Pigs Using Complete Blood Count Data From a Natural Disease Challenge Model","year":2020,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Animal Disease Management and Epidemiology","field":"Agricultural and Biological Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre de Développement du Porc du Québec; University of Saskatchewan; University of Alberta","funders":"Ministero dello Sviluppo Economico; University of Alberta; Swine Innovation Porc; Genome Alberta; Alberta Agriculture and Forestry; Genome Canada","keywords":"Disease; Phenotype; Count data; Resilience (materials science); Biology; Complete blood count; Clinical phenotype; Computational biology; Genetics; Medicine; Immunology; Statistics; Gene; Internal medicine; 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":[0.0009395679,0.0003122381,0.0003347975,0.0007621926,0.000289154,0.0003983661,0.0001991721,0.0004311545,0.0008214219],"category_scores_gemma":[0.001308364,0.0001484297,0.0003474766,0.0003723222,0.0003638104,0.0003089042,0.0003754051,0.000509712,0.0001572751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002323536,"about_ca_system_score_gemma":0.0001983574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001052507,"about_ca_topic_score_gemma":0.001893947,"domain_scores_codex":[0.9995302,0.0001390044,0.00003856429,0.0001401999,0.00008605696,0.00006600916],"domain_scores_gemma":[0.9986393,0.0002992404,0.000571135,0.0001748526,0.0001398823,0.0001755242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001780451,0.0002804434,0.8130254,0.00005638271,0.0001441701,0.0002768596,0.0003364173,0.0004327521,0.1778555,0.00006168075,0.0001439595,0.00560598],"study_design_scores_gemma":[0.00000508814,0.0008445127,0.9946391,0.000005185403,0.00002583761,0.0001944678,0.0001602678,0.0004049399,0.003538103,0.00003066391,0.0001443878,0.000007349954],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987602,0.0000442526,0.0007044066,0.00001074513,0.000002814644,0.00001580756,0.0003319733,0.000004737233,0.0001250545],"genre_scores_gemma":[0.997901,0.00003262859,0.0008677593,0.00002337123,0.000003953589,0.00005241744,0.0009393258,0.000003604586,0.0001759958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001052507,"threshold_uncertainty_score":0.004968941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3162901475802611,"score_gpt":0.2887260643933968,"score_spread":0.02756408318686426,"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."}}