{"id":"W4411721164","doi":"10.1101/2025.06.24.661262","title":"Generation of virtual populations for quantitative systems pharmacology through advanced sampling methods","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Pfizer","keywords":"Sampling (signal processing); Systems pharmacology; Computer science; Pharmacology; Computational biology; Medicine; Medical physics; Data science; Biology; Drug; 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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005167882,0.0005653536,0.0018532,0.000247562,0.0001977048,0.00008071659,0.0005701933,0.0008355489,0.00003166169],"category_scores_gemma":[0.05511736,0.0005952565,0.0003606575,0.0004956495,0.0002124573,0.0001284491,0.0004023567,0.0007038942,0.000003055171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000267605,"about_ca_system_score_gemma":0.0005929175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002216463,"about_ca_topic_score_gemma":0.0000010547,"domain_scores_codex":[0.9935231,0.002273455,0.002268296,0.001100085,0.0003508556,0.0004841916],"domain_scores_gemma":[0.9668742,0.02859647,0.00176863,0.001014991,0.001607899,0.0001377658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001450455,0.0002197411,0.00002720631,0.001639693,0.0004485442,0.000001413669,0.00002483395,0.002550878,0.4218825,0.5723588,0.0006796019,0.00002167543],"study_design_scores_gemma":[0.005345313,0.001014742,0.0005314688,0.002560381,0.004235962,1.790556e-8,0.00005477717,0.06711967,0.8491599,0.06212425,0.005340943,0.002512541],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02128693,0.0006435222,0.961256,0.0001244969,0.0106342,0.003127646,0.002676338,0.0002395525,0.00001134289],"genre_scores_gemma":[0.03635532,0.00009643676,0.9611257,0.0001160289,0.0007967638,0.001395319,0.000001069864,0.0001020254,0.00001129008],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5102346,"threshold_uncertainty_score":0.9996499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.643959710657186,"score_gpt":0.5826955811956139,"score_spread":0.06126412946157211,"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."}}