{"id":"W4400457746","doi":"10.1128/msphere.00360-24","title":"PUPpy: a primer design pipeline for substrain-level microbial detection and absolute quantification","year":2024,"lang":"en","type":"article","venue":"mSphere","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; University of British Columbia","funders":"Michael Smith Health Research BC; Canada Foundation for Innovation; BC Children's Hospital; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Puppy; Biology; Computational biology; Metagenomics; Primer (cosmetics); Microbiome; Genetics; Gene; Ecology; Chemistry","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002785084,0.00008595421,0.00008447038,0.00001234353,0.0001816147,0.00002685124,0.00009682275,0.0001089368,0.001625805],"category_scores_gemma":[0.00002254844,0.00008244412,0.00003265668,0.00008492241,0.0001231117,0.00008856213,0.00005123724,0.0001269276,0.0002939717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000417518,"about_ca_system_score_gemma":0.00001067582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000194594,"about_ca_topic_score_gemma":0.002154857,"domain_scores_codex":[0.9993965,0.00008790781,0.0001240577,0.0002078197,0.00002774566,0.0001559469],"domain_scores_gemma":[0.9996698,0.0001324853,0.00002651843,0.000134839,0.000005973762,0.00003032551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007170486,0.00003001669,0.00004801985,0.00002411051,0.00001195701,8.982258e-7,0.0002650979,0.0001907804,0.9331663,0.00009102206,0.03124929,0.03485079],"study_design_scores_gemma":[0.001412436,0.0006083671,0.06291983,0.00007296883,0.0001779242,0.0001269882,0.0002357539,0.07021555,0.2556593,0.01377156,0.5938202,0.0009791248],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6824929,0.0003402644,0.3134478,0.001125505,0.000652671,0.0007781432,0.00005853009,0.0001329499,0.0009712118],"genre_scores_gemma":[0.9931265,0.00001934637,0.004574237,0.0001485625,0.0000622706,0.00002810708,0.00003213222,0.00001280345,0.001996086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.677507,"threshold_uncertainty_score":0.9992868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04823156810349982,"score_gpt":0.2626261053436916,"score_spread":0.2143945372401918,"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."}}