{"id":"W2579943635","doi":"10.1142/s0218339017500061","title":"JOINT IMPACTS OF THERAPY DURATION, DRUG EFFICACY AND TIME LAG IN IMMUNE EXPANSION ON IMMUNITY BOOSTING BY ANTIVIRAL THERAPY","year":2017,"lang":"en","type":"article","venue":"Journal of Biological Systems","topic":"Immune Cell Function and Interaction","field":"Immunology and Microbiology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Tongji University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Immunity; Immune system; Boosting (machine learning); Immunology; Phase lag; Lag; Medicine; Pharmacotherapy; Artificial intelligence; Computer science; Internal medicine; 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":[0.0008580773,0.0004091844,0.0006141356,0.0003257133,0.0002418541,0.0007784486,0.0003581911,0.0007434846,0.002636093],"category_scores_gemma":[0.003309005,0.0002657743,0.0005777811,0.0001833103,0.0006674477,0.0009673159,0.0005795278,0.0007304915,0.0001778452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006162364,"about_ca_system_score_gemma":0.0009532161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001409063,"about_ca_topic_score_gemma":0.001108288,"domain_scores_codex":[0.9997088,0.0000853247,0.00001347874,0.00003382472,0.0000452447,0.00011319],"domain_scores_gemma":[0.9984937,0.001115928,0.0001865308,0.00003944908,0.00006693947,0.00009754024],"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":[0.0008700989,0.000435241,0.004590733,0.0003296518,0.00007112164,0.0004589124,0.00009156845,0.8791252,0.0764229,0.02162028,0.0004713495,0.01551292],"study_design_scores_gemma":[0.0001361281,0.001235229,0.004260032,0.00007012337,0.0001545084,0.0001426409,0.000174741,0.9533215,0.03014203,0.008522159,0.001774719,0.00006628006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9365795,0.002572542,0.04876025,0.0005889626,0.0001163852,0.0001240062,0.0001600984,0.00009998514,0.01099834],"genre_scores_gemma":[0.9961314,0.0004130738,0.00226658,0.00003779561,0.000007222195,0.00003885791,0.0000194605,0.0000128375,0.001072718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002636093,"threshold_uncertainty_score":0.008818626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0319930630628246,"score_gpt":0.2680472022237262,"score_spread":0.2360541391609016,"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."}}