{"id":"W4380224791","doi":"10.1101/2023.06.11.544497","title":"Comparing kinetic proofreading and kinetic segregation for T cell receptor activation","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"T-cell and B-cell Immunology","field":"Immunology and Microbiology","cited_by":0,"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; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"T-cell receptor; In silico; Antigen; Proofreading; Computational biology; Immune system; Biology; T cell; Receptor; Biological system; Cell biology; Computer science; Biophysics; Immunology; Genetics; Gene","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":[0.001583637,0.0006264347,0.0008134104,0.0004892668,0.0003794253,0.001471731,0.001124743,0.000980019,0.003350269],"category_scores_gemma":[0.009898391,0.0003359242,0.0007870481,0.000477354,0.0007730304,0.00221434,0.0005749366,0.0009880168,0.0004834305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001638104,"about_ca_system_score_gemma":0.001035729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004079266,"about_ca_topic_score_gemma":0.001545126,"domain_scores_codex":[0.9995111,0.0001731634,0.00003122995,0.00008554506,0.0001009382,0.00009798796],"domain_scores_gemma":[0.9882004,0.009896466,0.0007010778,0.0004040236,0.0004838617,0.0003142435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003672922,0.0001317952,0.002874381,0.0001157663,0.00002969018,0.00009780774,0.00005524393,0.9760389,0.005610112,0.009103843,0.0003157266,0.005259369],"study_design_scores_gemma":[0.00002915332,0.0001065075,0.0003193877,0.000005941525,0.00001164871,0.0000190709,0.00001847733,0.9940136,0.002321027,0.003009433,0.0001293042,0.00001641323],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.945246,0.0007115109,0.04606779,0.0005746839,0.00008313904,0.00005142222,0.0001261951,0.0001916443,0.00694769],"genre_scores_gemma":[0.9959637,0.0001494081,0.003169018,0.00003319506,0.000008337215,0.00002774167,0.00006384897,0.00002489659,0.0005599413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004079266,"threshold_uncertainty_score":0.01188529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02469802016888263,"score_gpt":0.2210229633889153,"score_spread":0.1963249432200327,"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."}}