{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002073548,0.0005603536,0.0005134749,0.002365791,0.0006161157,0.001761884,0.0004830619,0.0008510817,0.001999679],"category_scores_gemma":[0.006213284,0.000313173,0.0005106345,0.001956312,0.001208509,0.000850753,0.000793283,0.0008808718,0.0009655567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004050782,"about_ca_system_score_gemma":0.0005494134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001219512,"about_ca_topic_score_gemma":0.002494655,"domain_scores_codex":[0.9965352,0.0005018513,0.0003003011,0.001129578,0.001355588,0.0001774663],"domain_scores_gemma":[0.9930069,0.002096212,0.00249661,0.0004719641,0.001765006,0.0001632802],"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":[0.0007808162,0.0001886705,0.1807116,0.001070625,0.0002258922,0.0001738053,0.001250767,0.000993783,0.748831,0.0004695122,0.0003374794,0.06496606],"study_design_scores_gemma":[0.000018206,0.00128119,0.645438,0.0002457583,0.0003089315,0.0009101685,0.001437847,0.00249574,0.3336513,0.001111294,0.01300954,0.00009207898],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.950301,0.003173897,0.0382419,0.0002928333,0.0000734713,0.0002440198,0.003813827,0.0002607881,0.003598233],"genre_scores_gemma":[0.9402827,0.001359215,0.05004637,0.0003586276,0.00004512575,0.0002530583,0.004846927,0.0001651787,0.002642792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002365791,"threshold_uncertainty_score":0.01096606,"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."}}