{"id":"W2412584429","doi":"10.1007/978-1-60327-545-3_2","title":"High-Throughput Screening of Model Bacteria","year":2009,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Bacterial Genetics and Biotechnology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"High-throughput screening; Bacteria; Throughput; Computational biology; Biochemical engineering; Small molecule; Biology; Chemistry; Nanotechnology; Biochemistry; Computer science; Engineering; Genetics; Materials science","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":[0.001240097,0.001027831,0.001613296,0.000862254,0.0004146009,0.0008753001,0.001007731,0.0009718733,0.001564315],"category_scores_gemma":[0.0006350245,0.0003580319,0.0008438738,0.000947017,0.000265595,0.0005263725,0.000755044,0.0009614561,0.001609514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004175486,"about_ca_system_score_gemma":0.0004569231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007699765,"about_ca_topic_score_gemma":0.001456729,"domain_scores_codex":[0.9983695,0.0005793897,0.0001001404,0.0002099159,0.0006145583,0.0001264864],"domain_scores_gemma":[0.9995004,0.0002115276,0.00005760429,0.0001004624,0.00008180322,0.000048207],"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":[0.0001090371,0.0001752869,0.0001787301,0.0001954647,0.00002492725,0.00006096941,0.00001991895,0.0007428061,0.9942092,0.0002224913,0.0003238236,0.003737463],"study_design_scores_gemma":[0.00004173605,0.0009263054,0.001102951,0.00002029044,0.00006362482,0.0001883846,0.00002223154,0.004358423,0.9871104,0.0001989821,0.005947042,0.00001951142],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6375845,0.01594647,0.3137051,0.001050327,0.0003956842,0.002840386,0.01338675,0.003879882,0.01121095],"genre_scores_gemma":[0.6958373,0.01785315,0.2474385,0.0004045514,0.0001301871,0.003233019,0.0169055,0.0003820617,0.01781578],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001613296,"threshold_uncertainty_score":0.006558359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02269790749431416,"score_gpt":0.3585709843907485,"score_spread":0.3358730768964344,"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."}}