{"id":"W4403503812","doi":"10.1016/j.xpro.2024.103397","title":"Protocol for generating high-fidelity proteomic profiles using DROPPS","year":2024,"lang":"en","type":"article","venue":"STAR Protocols","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Princess Margaret Cancer Centre","funders":"Canadian Institutes of Health Research; Canada Research Chairs; Terry Fox Research Institute; Canadian Cancer Society","keywords":"Protocol (science); Computer science; Computational biology; Fidelity; Biology; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003203462,0.0003013765,0.0002631504,0.00005626125,0.0003344155,0.0002446549,0.0003123348,0.0001833571,0.0003402924],"category_scores_gemma":[0.00005764055,0.0002703675,0.0001407661,0.0001738911,0.00007392436,0.0002302697,0.0001179468,0.0003266659,0.00001439381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002368429,"about_ca_system_score_gemma":0.000295589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003980235,"about_ca_topic_score_gemma":0.000002352231,"domain_scores_codex":[0.9980989,0.0000207764,0.0005697355,0.000670378,0.0002009862,0.0004393021],"domain_scores_gemma":[0.9989743,0.00004882184,0.0001730077,0.0005644881,0.0001459216,0.00009349718],"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":[0.00008851242,0.00007528973,0.00004470213,0.002990948,0.00002211236,0.000002942342,0.00003987449,0.0001342991,0.9800308,0.01029783,0.0006988136,0.005573873],"study_design_scores_gemma":[0.0006059087,0.00007164074,0.000001768657,0.0009166634,0.000010483,0.000007855105,0.00001358699,0.03880283,0.8531231,0.02859323,0.07749829,0.000354615],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.005723115,0.000002083441,0.1563524,0.00009117091,0.000009908094,0.8363392,0.0002121416,0.0007824802,0.0004875244],"genre_scores_gemma":[0.0007527299,7.174862e-8,0.3657763,0.00002151124,0.000347169,0.6325922,0.00002577157,0.00006809442,0.000416175],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.2094239,"threshold_uncertainty_score":0.9999748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06602641676116533,"score_gpt":0.4091775387148643,"score_spread":0.343151121953699,"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."}}