{"id":"W4366177285","doi":"10.1101/2023.04.12.536622","title":"A calibration and uncertainty quantification analysis of classical, fractional and multiscale logistic models of tumour growth","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Identifiability; Inference; Bayesian probability; Logistic regression; Bayesian inference; Expression (computer science); Growth model; Calibration; Population; Computational biology; Biological system; Computer science; Logistic function; Mathematics; Applied mathematics; Statistics; Biology; Artificial intelligence; Medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009117482,0.0003582638,0.0010821,0.0005788748,0.00007540987,0.00004829514,0.0002384549,0.0004580111,0.0000163037],"category_scores_gemma":[0.001932825,0.0003397535,0.0001641634,0.0007605511,0.0004533122,0.0001188544,0.0003169311,0.0004037854,0.000001614087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007140145,"about_ca_system_score_gemma":0.0001601701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000105662,"about_ca_topic_score_gemma":0.00001588813,"domain_scores_codex":[0.9974244,0.0002236896,0.0009879275,0.0007440671,0.0003702603,0.0002496657],"domain_scores_gemma":[0.9964511,0.001260747,0.0009947462,0.0006204665,0.0005260059,0.000146972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001061809,0.0006563373,0.01222107,0.00486915,0.002343368,0.00001293288,0.00007562952,0.001897474,0.4631358,0.5144014,0.000279725,9.137283e-7],"study_design_scores_gemma":[0.0005313779,0.00008404811,0.09483948,0.0005037695,0.003232123,3.658702e-8,0.00001993955,0.8377551,0.0466396,0.01570328,0.000003896923,0.0006873487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8268212,0.0001222898,0.17131,0.0002488214,0.0001146831,0.0005368132,0.000682293,0.0001596071,0.000004262054],"genre_scores_gemma":[0.9835095,0.0001291057,0.01614462,0.00001262653,0.00004343945,0.00009945976,0.00000312883,0.00005386631,0.000004218383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8358576,"threshold_uncertainty_score":0.9999055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0774985264063663,"score_gpt":0.2932187147602345,"score_spread":0.2157201883538682,"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."}}