{"id":"W4417131431","doi":"10.1007/s10928-025-10009-4","title":"Identification and characterization of virtual sub-populations through phenotype-guided filtering. The challenging case of nonidentifiable models in the context of therapeutic evaluation","year":2025,"lang":"en","type":"article","venue":"Journal of Pharmacokinetics and Pharmacodynamics","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Identification (biology); Context (archaeology); Quality (philosophy); Parametric statistics; Parametric model; Context model","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001061865,0.00009929747,0.0001967303,0.0001068998,0.00005986369,0.0000160953,0.0001445505,0.00004803943,0.000003340307],"category_scores_gemma":[0.0000169037,0.00007355307,0.00007315214,0.0002286025,0.00009376056,0.00002363654,0.00004458173,0.00009627428,1.868542e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000125828,"about_ca_system_score_gemma":0.00005245692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001631731,"about_ca_topic_score_gemma":0.00001711325,"domain_scores_codex":[0.9987528,0.0002110164,0.0006586366,0.0001100922,0.0001841655,0.00008328567],"domain_scores_gemma":[0.9987411,0.00004092355,0.0006396079,0.000147065,0.0004145966,0.00001668739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000642946,0.00007258503,0.0004090097,0.00006874572,0.000172194,0.000001369203,0.0007268594,0.04768651,0.9418339,0.0006803927,0.00001991578,0.008264179],"study_design_scores_gemma":[0.001160911,0.00007156569,0.002827812,0.00005537724,0.0007746621,0.00006365219,0.0006682063,0.7487007,0.2446252,0.0008752438,0.00009238248,0.00008428975],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9739692,0.002526834,0.02288652,0.0001899757,0.0001558283,0.0002282375,0.00002340734,7.038356e-7,0.00001927656],"genre_scores_gemma":[0.9957243,0.004050732,0.00006236242,0.00006350047,0.00004760058,0.000005198227,0.00002709251,0.000007964572,0.00001129666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7010142,"threshold_uncertainty_score":0.2999407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03390567121742349,"score_gpt":0.3319367555655703,"score_spread":0.2980310843481468,"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."}}