{"id":"W3040192468","doi":"10.1016/j.xpro.2020.100057","title":"High-Throughput Chemical Screening for Inhibitors of Salmonella Pathogenicity Island 2","year":2020,"lang":"en","type":"article","venue":"STAR Protocols","topic":"Salmonella and Campylobacter epidemiology","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Canadian Institutes of Health Research","keywords":"Virulence; Salmonella; High-throughput screening; Identification (biology); Biology; Pathogenicity island; Throughput; Computational biology; Protocol (science); Screening techniques; Pathogenicity; Microbiology; Genetics; Bacteria; Gene; Bioinformatics; Computer science; Medicine; Telecommunications","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":[0.0002834522,0.0001608631,0.0003895579,0.000004667937,0.00006864173,0.00001522873,0.000253958,0.000153421,0.0001666969],"category_scores_gemma":[0.0002292903,0.00006693702,0.000179707,0.0001798556,0.00008058199,0.00005805549,0.0001360586,0.000112492,0.000008784076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009162218,"about_ca_system_score_gemma":0.000009482987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008236157,"about_ca_topic_score_gemma":0.00001922118,"domain_scores_codex":[0.998658,0.00009810074,0.0004160718,0.0003786915,0.0001386703,0.0003104683],"domain_scores_gemma":[0.99924,0.0002947797,0.0001895331,0.0000624631,0.00007750269,0.0001356861],"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":[0.00044498,0.00006451594,0.01188254,0.00005750993,0.00002255826,0.000002099424,0.0001030314,0.000006466443,0.9604851,0.0002884232,0.003898246,0.02274456],"study_design_scores_gemma":[0.002851991,0.004177547,0.04944295,0.0001958997,0.00005783964,0.00001082395,0.0004163923,0.0006396538,0.8082167,0.00943849,0.1235256,0.001026087],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9714476,0.00002125052,0.0004407819,0.001911999,0.00003375241,0.02567141,0.0003045275,0.00006746544,0.0001012326],"genre_scores_gemma":[0.9803656,0.000002240871,0.003766453,0.0007681672,0.0008235702,0.01413892,0.0001155032,0.000002708034,0.00001683204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1522684,"threshold_uncertainty_score":0.2729612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06961064434213207,"score_gpt":0.2887117031223261,"score_spread":0.2191010587801941,"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."}}