{"id":"W4389925201","doi":"10.1101/2023.12.18.572184","title":"PUPpy: a primer design pipeline for substrain-level microbial detection and absolute quantification.","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; University of British Columbia","funders":"","keywords":"Puppy; Biology; Metagenomics; 16S ribosomal RNA; Shotgun sequencing; Computational biology; Microbial population biology; Genetics; DNA sequencing; Bacteria; Ecology; Gene","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":[0.004980263,0.004189899,0.002221971,0.002403573,0.0009895449,0.001964672,0.003139039,0.002025564,0.0257851],"category_scores_gemma":[0.009306055,0.003323807,0.002569711,0.001106113,0.001178944,0.002112667,0.003306061,0.004903325,0.02172703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008032707,"about_ca_system_score_gemma":0.003441862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001110054,"about_ca_topic_score_gemma":0.001672555,"domain_scores_codex":[0.9962643,0.0008913355,0.0003323605,0.001061349,0.001136198,0.0003145115],"domain_scores_gemma":[0.9973218,0.001310731,0.0004729693,0.0003202023,0.0004186912,0.0001556233],"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":[0.002360694,0.0006425813,0.006735315,0.007607851,0.001160271,0.001997063,0.001312831,0.0118667,0.4162409,0.00871723,0.2413523,0.3000062],"study_design_scores_gemma":[0.0006807307,0.0009265101,0.005217104,0.0007594668,0.0003343766,0.003136741,0.0001826672,0.1110892,0.4565449,0.01993345,0.4005095,0.0006853347],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004409252,0.001312082,0.8058241,0.0002902296,0.0003554889,0.0007073673,0.01407495,0.1709351,0.00209145],"genre_scores_gemma":[0.02060574,0.0008455733,0.9265078,0.00123736,0.00007718305,0.004455531,0.02167326,0.01876903,0.005828572],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0257851,"threshold_uncertainty_score":0.08625972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05146169591662744,"score_gpt":0.264160658443993,"score_spread":0.2126989625273656,"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."}}