{"id":"W811006216","doi":"10.1016/j.mimet.2015.06.018","title":"Expeditious screening of candidate proteins for microbial vaccines","year":2015,"lang":"en","type":"article","venue":"Journal of Microbiological Methods","topic":"Microbial infections and disease research","field":"Immunology and Microbiology","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Genome Alberta; Alberta Livestock and Meat Agency","keywords":"Biology; Immunogenicity; In silico; Recombinant DNA; Computational biology; Gene; Protein subunit; Antigenicity; Candidate gene; Molecular biology; Antibody; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002902341,0.0002061205,0.0007081386,0.0002672912,0.000119968,0.00002005757,0.0003575525,0.0003397147,0.0002742881],"category_scores_gemma":[0.001686875,0.0001404134,0.000425243,0.0002178436,0.0002424696,0.0001097399,0.0001269721,0.0003960466,0.00001339763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000562045,"about_ca_system_score_gemma":0.0002447863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000602085,"about_ca_topic_score_gemma":0.000006017533,"domain_scores_codex":[0.9974346,0.001011179,0.0008877355,0.0002174098,0.00003689709,0.0004122301],"domain_scores_gemma":[0.9974691,0.0006067214,0.0006400988,0.0001915988,0.000981853,0.0001106564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001311343,0.0002951415,0.0003496915,0.00003200334,0.0002406396,0.000005471756,0.00010206,0.00002797694,0.9660977,0.0001280693,0.01911084,0.01229903],"study_design_scores_gemma":[0.004210204,0.002010639,0.0002883871,0.00008908313,0.0001193979,0.0008350272,0.0002468349,0.000003507884,0.8828427,0.0008106722,0.1083256,0.0002179057],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6080958,0.007862453,0.3804196,0.0004760169,0.001800419,0.0008217164,0.0002901817,0.00003548447,0.000198395],"genre_scores_gemma":[0.6018966,0.000210415,0.3962752,0.000215616,0.0005318674,0.00003452383,0.00008985492,0.00003546044,0.0007105194],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08921477,"threshold_uncertainty_score":0.5725892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.106069484302009,"score_gpt":0.4198459702100777,"score_spread":0.3137764859080688,"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."}}