{"id":"W3122371410","doi":"10.20944/preprints201911.0081.v1","title":"The Zebrafish Xenograft Platform – A Novel Tool for Modeling KSHV-Associated Diseases","year":2019,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Zebrafish Biomedical Research Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Beatrice Hunter Cancer Research Institute; Canadian Bioethics Society; University of Ottawa; Dalhousie University","funders":"Canadian Institutes of Health Research; Cure Brain Cancer Foundation; Nova Scotia Health Research Foundation; Canadian Imperial Bank of Commerce; University of California, San Francisco; Dalhousie University; École Polytechnique Fédérale de Lausanne; Beatrice Hunter Cancer Research Institute; Cancer Research Institute","keywords":"Lytic cycle; Zebrafish; Biology; Primary effusion lymphoma; Virology; Virus latency; Cancer research; Oncolytic virus; Cell biology; Viral replication; Virus; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001152427,0.0004763447,0.0004391796,0.00008955853,0.0004541524,0.00012552,0.00181626,0.000774865,0.00007025574],"category_scores_gemma":[0.004657917,0.000403239,0.0005721733,0.0001698688,0.0002728291,0.000009896881,0.003765137,0.0007700181,0.0002944434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001628967,"about_ca_system_score_gemma":0.0008573034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008927589,"about_ca_topic_score_gemma":0.0001325255,"domain_scores_codex":[0.9962198,0.00007293483,0.0007048158,0.001413777,0.000667419,0.0009212866],"domain_scores_gemma":[0.9960389,0.0002549766,0.0003417491,0.002433487,0.0005905339,0.0003403482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001943242,0.003260326,0.1253864,0.001276961,0.00461201,0.000003691973,0.0004269084,0.01491207,0.8189812,0.002139743,0.02049568,0.006561745],"study_design_scores_gemma":[0.007130653,0.0003608045,0.08265018,0.0007357208,0.0007338668,0.00001442166,0.0002446927,0.06291556,0.07287747,0.01828702,0.7501462,0.003903373],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9535755,0.0005229585,0.03838095,0.00151059,0.0004158269,0.003293581,0.001341574,0.0001232184,0.0008358151],"genre_scores_gemma":[0.9884951,0.0006154174,0.0005350964,0.000346451,0.0004727905,0.002543359,0.003620012,0.0001112476,0.003260598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7461037,"threshold_uncertainty_score":0.9998419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1086506015774632,"score_gpt":0.3725204475012923,"score_spread":0.2638698459238291,"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."}}