{"id":"W4388918032","doi":"10.1016/j.ifacol.2023.10.330","title":"Nonlinear Impulsive Control Design for Biologically Grounded NSM Tumor Growth Model Using Exact Linearized Mapping","year":2023,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"Army Research Office; Engineering and Physical Sciences Research Council; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Breast Cancer Research Foundation","keywords":"Control theory (sociology); Computer science; Nonlinear system; Scheduling (production processes); Controller (irrigation); Observer (physics); Control (management); Control engineering; Mathematical optimization; Mathematics; Engineering; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00189749,0.0007052266,0.001382737,0.0003173684,0.0003881129,0.00008463069,0.0006649892,0.000394562,0.00009824299],"category_scores_gemma":[0.006311641,0.0005442179,0.0005119836,0.0006801057,0.0002835908,0.0001730741,0.000189308,0.0004876871,0.0001211309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001664191,"about_ca_system_score_gemma":0.0002148228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001553622,"about_ca_topic_score_gemma":0.000005353969,"domain_scores_codex":[0.9958808,0.0003321811,0.001178787,0.0009459323,0.0004024511,0.001259872],"domain_scores_gemma":[0.9933467,0.004792982,0.0005285181,0.0005965451,0.0004250537,0.0003101933],"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":[0.003794377,0.00306209,0.0005929355,0.003220292,0.002257707,0.0006782461,0.003201518,0.006244482,0.8973781,0.07739653,0.0007090736,0.001464692],"study_design_scores_gemma":[0.003621754,0.000337398,0.00003039551,0.0001254569,0.0001825444,0.00006144783,0.0002737304,0.8230423,0.002143616,0.169539,0.00002198517,0.0006203281],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2977229,0.00003930594,0.6964101,0.001150956,0.0001906942,0.002521823,0.0004854079,0.001240458,0.0002384583],"genre_scores_gemma":[0.07025824,0.000016111,0.9272864,0.0009507136,0.0004871734,0.0002481452,0.0001581823,0.0001428098,0.0004521996],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8952345,"threshold_uncertainty_score":0.999701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1356540145536748,"score_gpt":0.3392795307163242,"score_spread":0.2036255161626494,"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."}}