{"id":"W2952481240","doi":"10.1016/j.heliyon.2019.e01935","title":"Exploring DNA quantity and quality from raw materials to botanical extracts","year":2019,"lang":"en","type":"article","venue":"Heliyon","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"University of Guelph","keywords":"Ingredient; Raw material; Biotechnology; Biochemical engineering; Quality (philosophy); Sanger sequencing; Computer science; Computational biology; Biology; Food science; DNA; DNA sequencing; Engineering; Genetics; Ecology","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":[],"consensus_categories":[],"category_scores_codex":[0.0002690318,0.00009178784,0.000124381,0.00002289921,0.00004999212,0.00007088362,0.0001096445,0.00007766356,0.0002033215],"category_scores_gemma":[0.0001156546,0.00009396239,0.00002911063,0.00004756382,0.00002162371,0.00001044252,0.00007549461,0.0000394898,0.0005622423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006892744,"about_ca_system_score_gemma":0.00001460408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005832031,"about_ca_topic_score_gemma":0.0000643954,"domain_scores_codex":[0.9990495,0.00008483681,0.0002536658,0.0003697665,0.0001111461,0.0001310493],"domain_scores_gemma":[0.9993317,0.00001846276,0.00007002773,0.0004363871,0.00005727712,0.00008618107],"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":[0.00005779824,0.00002839426,0.002635499,0.00002995575,0.00001087984,1.414462e-7,0.00004988609,0.00000146718,0.9957359,0.001119652,0.0001280219,0.0002023771],"study_design_scores_gemma":[0.0001818629,0.00003664261,0.1869899,0.00002277418,0.000004159925,7.351659e-7,0.00006386737,5.376143e-7,0.7986882,0.000006240581,0.01389029,0.0001148031],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979936,0.0002420811,0.0004951435,0.0002029181,0.0006189863,0.0001749904,0.00003610761,0.00001872728,0.0002174266],"genre_scores_gemma":[0.9980294,0.0004377821,0.0002438149,0.0002895383,0.0001496438,0.00002415694,0.0001468816,0.00001338341,0.0006654045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1970477,"threshold_uncertainty_score":0.7226675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1323655996324239,"score_gpt":0.3280064553308065,"score_spread":0.1956408556983825,"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."}}