{"id":"W1996186193","doi":"10.1371/journal.pone.0089345","title":"Optimization of a One-Step Heat-Inducible In Vivo Mini DNA Vector Production System","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Viral Infectious Diseases and Gene Expression in Insects","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"In vivo; DNA; Vector (molecular biology); Production (economics); Computational biology; Biology; Biological system; Recombinant DNA; Biotechnology; Genetics; 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":[0.0007991959,0.0009509764,0.0007178645,0.0004359201,0.0002313815,0.0006076907,0.0006814425,0.0004618171,0.0008761198],"category_scores_gemma":[0.0006127268,0.0003801089,0.0004206153,0.0003581478,0.0002435243,0.0003859322,0.0003617881,0.0009042727,0.0007389273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004918358,"about_ca_system_score_gemma":0.0005282447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006366473,"about_ca_topic_score_gemma":0.001068528,"domain_scores_codex":[0.9992169,0.0001402226,0.0001198461,0.0002003292,0.0002229797,0.00009972176],"domain_scores_gemma":[0.9995856,0.00007434326,0.0001585481,0.00004573425,0.00007878467,0.00005703679],"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":[0.00004345252,0.00005736439,0.0001154028,0.00006397892,0.000004608754,0.0000177435,0.00001915299,0.0002591336,0.997903,0.0000485378,0.00003247239,0.00143521],"study_design_scores_gemma":[0.000008324316,0.0002959582,0.0009492512,0.000007896456,0.00002266781,0.00005374759,0.00001293321,0.000963565,0.995795,0.00001555571,0.00186298,0.00001218036],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8918931,0.001512506,0.1014996,0.0002152577,0.0001085722,0.001224035,0.001077805,0.0008485958,0.001620544],"genre_scores_gemma":[0.8028893,0.002378627,0.1833447,0.0001465254,0.00005191847,0.001534842,0.003976686,0.0004405161,0.005236869],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0009509764,"threshold_uncertainty_score":0.004226565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01889424949378349,"score_gpt":0.2209555261573531,"score_spread":0.2020612766635697,"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."}}