{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003437164,0.0007247539,0.0003584707,0.0006841694,0.0003522607,0.0004550577,0.0006582817,0.0006965973,0.002473544],"category_scores_gemma":[0.0001406747,0.0003715106,0.0004471735,0.000152308,0.0003604479,0.0004729126,0.0006118797,0.0009777625,0.0006192541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007027264,"about_ca_system_score_gemma":0.0006758812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003567569,"about_ca_topic_score_gemma":0.005381381,"domain_scores_codex":[0.9998395,0.00001420742,0.000007743136,0.00003545064,0.00007738836,0.00002563508],"domain_scores_gemma":[0.9998981,0.00001696716,0.00002584372,0.00001230744,0.00001263738,0.00003412945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001015375,0.00004477305,0.0004403333,0.00009448348,0.00001567926,0.000286645,0.00005985551,0.001567433,0.9891093,0.001393625,0.0006848125,0.006201616],"study_design_scores_gemma":[0.0001062022,0.00104632,0.003172639,0.00007242956,0.0001140241,0.001153748,0.00007398557,0.02455278,0.9101302,0.0005821491,0.05892679,0.00006871575],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6421402,0.005790378,0.3177076,0.00111913,0.0005978286,0.001175737,0.006317597,0.007760602,0.01739094],"genre_scores_gemma":[0.8360675,0.003969443,0.1386769,0.0001895347,0.00003653393,0.001200114,0.001949106,0.0004892548,0.01742157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003567569,"threshold_uncertainty_score":0.008274794,"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."}}