{"id":"W4249458361","doi":"10.1038/npre.2012.6876","title":"A quantitative model for efficient construction of lentiviral vectors with a unique clone site","year":2012,"lang":"en","type":"preprint","venue":"Nature Precedings","topic":"Virus-based gene therapy research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Insert (composites); Cloning (programming); clone (Java method); Computational biology; Mutagenesis; Biology; Viral vector; Mutant; Recombinant DNA; Molecular biology; Genetics; Gene; Computer science; Engineering","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":[0.001942493,0.001149961,0.0009615943,0.00121041,0.0003990725,0.00133142,0.001871242,0.001156167,0.00150837],"category_scores_gemma":[0.002426461,0.0009851317,0.0009927663,0.0008196395,0.001037627,0.001297811,0.000797765,0.002387386,0.001274675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001668939,"about_ca_system_score_gemma":0.0008250747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001082989,"about_ca_topic_score_gemma":0.0006190537,"domain_scores_codex":[0.9979501,0.0004187624,0.0001209746,0.0005431745,0.000822768,0.0001442278],"domain_scores_gemma":[0.9990008,0.0004516693,0.0001911568,0.0001468258,0.0001601105,0.00004946572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001317349,0.0001674289,0.0007362815,0.0002431489,0.00005624865,0.0001437626,0.0001224226,0.0213705,0.907582,0.04911285,0.0007223859,0.01961119],"study_design_scores_gemma":[0.00006955343,0.0006156389,0.0008923448,0.0000382489,0.000092585,0.0006480612,0.00003364876,0.4239569,0.5336542,0.02080509,0.01909281,0.0001009993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02645237,0.0004992492,0.9704787,0.000110991,0.00005343099,0.0001588369,0.0002837594,0.0006115859,0.001351119],"genre_scores_gemma":[0.3324652,0.001333159,0.6549222,0.0002363609,0.00007618328,0.001777685,0.001286444,0.0006343653,0.007268312],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001942493,"threshold_uncertainty_score":0.0121091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01995800019339663,"score_gpt":0.3228978274428463,"score_spread":0.3029398272494497,"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."}}