{"id":"W4409086045","doi":"10.1103/physrevresearch.7.023003","title":"Comparing kinetic proofreading and kinetic segregation for T cell receptor activation","year":2025,"lang":"en","type":"article","venue":"Physical Review Research","topic":"CAR-T cell therapy research","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Advanced Research Projects Agency; Defense Advanced Research Projects Agency; Oncode Institute; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Kinetic energy; Proofreading; Biophysics; Chemistry; Computational biology; Cell biology; Computer science; Biology; Physics; Biochemistry; Enzyme; Polymerase","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":[0.001158623,0.0005936162,0.0008100704,0.0005347352,0.0003846021,0.00144066,0.001081065,0.001139659,0.002966496],"category_scores_gemma":[0.006994817,0.000280363,0.0008901202,0.0004446687,0.0008721494,0.002554025,0.000579483,0.0008559757,0.0005443912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001465807,"about_ca_system_score_gemma":0.001050052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002132034,"about_ca_topic_score_gemma":0.0009478538,"domain_scores_codex":[0.9995401,0.0001307365,0.00002506466,0.00008593463,0.0001151473,0.0001031147],"domain_scores_gemma":[0.9958166,0.003159874,0.000381888,0.0002485002,0.0002249841,0.0001681249],"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":[0.0005295618,0.0002736719,0.00278695,0.0004012612,0.00005513589,0.0002937109,0.0001259275,0.875829,0.02201363,0.08018143,0.0008508767,0.0166587],"study_design_scores_gemma":[0.00004023215,0.0001560918,0.0005432341,0.00001391297,0.00002370005,0.00005494758,0.00003254636,0.9815516,0.00500147,0.0120136,0.0005383249,0.00003029913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8340806,0.004204025,0.1398855,0.001268208,0.0002726875,0.0001039807,0.0002127919,0.000322497,0.01964959],"genre_scores_gemma":[0.9908179,0.001131378,0.006344249,0.00007318376,0.00003102794,0.000057942,0.0001005308,0.00004594715,0.00139773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002966496,"threshold_uncertainty_score":0.01063526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1378438188406255,"score_gpt":0.4684592259087754,"score_spread":0.3306154070681499,"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."}}