{"id":"W4311244813","doi":"10.1103/physreve.106.l062402","title":"<i>In silico</i> testing of the universality of epithelial tissue growth","year":2022,"lang":"en","type":"article","venue":"Physical review. E","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Academy of Finland; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Universality (dynamical systems); Scaling; Scaling law; Kinetic energy; Dynamic scaling; Physics; Statistical physics; Condensed matter physics; Classical mechanics; Geometry; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004441102,0.0002810484,0.000358832,0.0002448325,0.0002397045,0.0006548844,0.0007579973,0.000727366,0.001557632],"category_scores_gemma":[0.00273915,0.0001773478,0.0004840788,0.0002004503,0.0007655512,0.0005870552,0.0004294305,0.0005270138,0.0002084796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000557795,"about_ca_system_score_gemma":0.0003408517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001853348,"about_ca_topic_score_gemma":0.0008446359,"domain_scores_codex":[0.9998417,0.00003941285,0.000008008979,0.00004674638,0.00003675725,0.00002739111],"domain_scores_gemma":[0.9989319,0.0006270306,0.0001492449,0.0001797517,0.00006949926,0.00004245642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001561316,0.0001709157,0.01121147,0.0005065655,0.00010776,0.0002678603,0.0001826889,0.8077833,0.08958448,0.07518938,0.001992672,0.01284682],"study_design_scores_gemma":[0.00001778846,0.00008676866,0.0017652,0.00001591759,0.00002067133,0.00005851001,0.00004127909,0.9661264,0.02084452,0.00997831,0.001027656,0.00001697101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8996693,0.0008181569,0.08063789,0.001695903,0.0001771794,0.00004148893,0.0006350288,0.0003254953,0.01599952],"genre_scores_gemma":[0.9941245,0.0002615801,0.004919003,0.00008034426,0.00001798921,0.00002392675,0.0001356238,0.00003196087,0.0004050769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001853348,"threshold_uncertainty_score":0.005210757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04161455951880867,"score_gpt":0.3454123616868368,"score_spread":0.3037978021680281,"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."}}