{"id":"W3184605023","doi":"10.1101/2021.07.19.452846","title":"Fighting cancer with oncolytic viral therapy: identifying threshold parameters for success","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Virus-based gene therapy research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Oncolytic virus; Virulence; Virus; Dynamics (music); Steady state (chemistry); Amplitude; Biology; Cancer therapy; Transmission (telecommunications); Cancer; Physics; Mechanics; Mathematics; Biological system; Control theory (sociology); Computer science; Statistical physics; Virology; Chemistry; Optics; Telecommunications; Genetics; Gene; Artificial intelligence; Control (management)","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":[0.0004535727,0.0005430042,0.0007553038,0.0005386691,0.0004189097,0.001147207,0.0007823007,0.001527707,0.0015885],"category_scores_gemma":[0.001897525,0.0003214525,0.0007186254,0.0002294669,0.00133335,0.0008906365,0.0008430732,0.0007925063,0.0001984077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001073435,"about_ca_system_score_gemma":0.000697926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003276131,"about_ca_topic_score_gemma":0.00130713,"domain_scores_codex":[0.9998271,0.00004841639,0.000006960516,0.00002867105,0.00003129708,0.00005757149],"domain_scores_gemma":[0.9994123,0.0002653015,0.0001841348,0.00002934906,0.00004149662,0.00006739569],"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":[0.00007470774,0.00005723864,0.001888819,0.00007027962,0.00002678434,0.0001863433,0.0001303221,0.9406714,0.01793171,0.03592136,0.0003703137,0.002670624],"study_design_scores_gemma":[0.00001330397,0.00005181311,0.0003902718,0.000009309063,0.00001154844,0.00002968232,0.00003436625,0.9916269,0.001315243,0.006175993,0.000330639,0.00001090857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7514689,0.001198119,0.2249414,0.001143437,0.00005450808,0.0001471116,0.00017481,0.0001447386,0.02072703],"genre_scores_gemma":[0.9923456,0.0002302125,0.004474673,0.00005060146,0.000009684185,0.0001083206,0.00002392116,0.00001541535,0.002741489],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003276131,"threshold_uncertainty_score":0.00778836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03227587108339729,"score_gpt":0.2975396950145396,"score_spread":0.2652638239311423,"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."}}