{"id":"W4382793075","doi":"10.1101/2023.06.29.546934","title":"Competitive Amplification Networks enable molecular pattern recognition with PCR","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Cancer Research","funders":"","keywords":"Amplicon; Computational biology; Scalability; Computer science; Context (archaeology); Signature (topology); Workflow; Gene expression; Molecular diagnostics; Polymerase chain reaction; Gene; Biology; Data mining; Bioinformatics; Genetics; Database; Mathematics","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":[0.001735439,0.001365612,0.0008530225,0.000946029,0.0004454073,0.001968067,0.001510099,0.001491954,0.01322685],"category_scores_gemma":[0.004658266,0.0007814219,0.0006667643,0.0009188382,0.001452884,0.001768041,0.00149637,0.002000555,0.005647778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001459836,"about_ca_system_score_gemma":0.0005984158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001338138,"about_ca_topic_score_gemma":0.001207749,"domain_scores_codex":[0.9975947,0.0006028119,0.00009092822,0.0007120792,0.0008247936,0.0001746532],"domain_scores_gemma":[0.9974069,0.001633018,0.0002151027,0.0002728458,0.000370532,0.0001016549],"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":[0.0006036322,0.0002708002,0.001241117,0.0006857436,0.0001394431,0.0002997254,0.0001488811,0.1398918,0.4756386,0.150264,0.01524496,0.2155713],"study_design_scores_gemma":[0.00004068101,0.0001016254,0.000280532,0.00002971618,0.00002357366,0.0001358744,0.0000178753,0.8217605,0.1280638,0.03076105,0.01874192,0.00004288081],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009005697,0.0005656523,0.9777461,0.0005840886,0.000241833,0.0001759283,0.0003193215,0.00299363,0.008367656],"genre_scores_gemma":[0.3329921,0.0009889296,0.6498701,0.0008305161,0.0002184198,0.0008413239,0.0007624512,0.0004015334,0.01309463],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01322685,"threshold_uncertainty_score":0.04424816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01516435732395549,"score_gpt":0.228700499855913,"score_spread":0.2135361425319575,"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."}}