{"id":"W4414625112","doi":"10.1088/2632-2153/ae11f8","title":"Generative diffusion model surrogates for mechanistic agent-based biological models","year":2025,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; TRIUMF","funders":"Advanced Scientific Computing Research; National Institute of Biomedical Imaging and Bioengineering; National Institute of General Medical Sciences","keywords":"Leverage (statistics); Surrogate model; Generative model; Probabilistic logic; Computational model; Classifier (UML); Generative grammar; Model selection","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":[0.0003072213,0.0001276626,0.0001378553,0.0002040804,0.000461191,0.00003427358,0.0002504953,0.0001846905,0.000001650021],"category_scores_gemma":[0.0003287635,0.0001033986,0.00003231348,0.0003429576,0.0005053375,0.000004988241,0.0001183149,0.0001413902,4.407374e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001605958,"about_ca_system_score_gemma":0.0001368611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000170917,"about_ca_topic_score_gemma":0.00002423438,"domain_scores_codex":[0.9990575,0.00001994211,0.000123924,0.0004546844,0.00008046356,0.0002634942],"domain_scores_gemma":[0.9995909,0.00001995835,0.00003966808,0.0001535921,0.0001592905,0.00003656841],"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":[0.00004410825,0.0000455986,0.003240619,0.00001017269,0.000006770984,4.265349e-7,0.00001239265,0.007387368,0.9667296,0.01699836,0.00001988752,0.005504656],"study_design_scores_gemma":[0.0004595302,0.0003294362,0.00003614759,0.000008709713,0.000008568039,0.000001529005,0.0000265515,0.775078,0.2116093,0.01164644,0.000684374,0.0001113643],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6281387,0.0003254409,0.3701769,0.0008942051,0.00006237773,0.0001553076,0.000008063552,0.00004901371,0.0001899709],"genre_scores_gemma":[0.990561,0.00008123562,0.008654946,0.000306097,0.0000131393,0.00003925325,0.00003491755,0.000007034534,0.0003023775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7676907,"threshold_uncertainty_score":0.4216473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01842598599032087,"score_gpt":0.2583001288849917,"score_spread":0.2398741428946708,"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."}}