{"id":"W2768069935","doi":"10.1016/j.apsb.2017.10.001","title":"Biomonitoring for traditional herbal medicinal products using DNA metabarcoding and single molecule, real-time sequencing","year":2017,"lang":"en","type":"article","venue":"Acta Pharmaceutica Sinica B","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Chinese Academy of Medical Sciences; National Natural Science Foundation of China","keywords":"Biotechnology; Counterfeit; Traditional medicine; Single molecule real time sequencing; Computational biology; DNA sequencing; Biomonitoring; Biology; Medicine; Genetics; DNA; Geography; Ecology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001203833,0.0009419175,0.0005496047,0.0024238,0.0005541053,0.0007985066,0.0005582034,0.001013469,0.0006003822],"category_scores_gemma":[0.002033375,0.0003750749,0.001035373,0.001701311,0.0005118218,0.0007643921,0.0006288793,0.0007695045,0.0004365815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004981183,"about_ca_system_score_gemma":0.0009642972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0028985,"about_ca_topic_score_gemma":0.006969311,"domain_scores_codex":[0.9982806,0.0002648915,0.0001547737,0.0007014583,0.0005283966,0.00006977833],"domain_scores_gemma":[0.9988124,0.0001843044,0.0004813522,0.0001120686,0.0003716111,0.00003826285],"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":[0.0001591855,0.00009389166,0.01674622,0.0009234961,0.0002261541,0.0002845837,0.0004130599,0.001446928,0.8958139,0.000878813,0.0006711469,0.08234261],"study_design_scores_gemma":[0.00003694973,0.0006904388,0.06847595,0.0002124358,0.0005841514,0.001110918,0.0004649309,0.02708719,0.8682803,0.001465026,0.03142677,0.0001649539],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6237295,0.01473075,0.3404517,0.001241705,0.0003725604,0.0007120194,0.008306951,0.002280171,0.008174639],"genre_scores_gemma":[0.4839816,0.005656901,0.4978584,0.001042751,0.00009259585,0.0003864788,0.006051249,0.0001900514,0.004739836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0028985,"threshold_uncertainty_score":0.006366551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2586580007416192,"score_gpt":0.3900456092481538,"score_spread":0.1313876085065346,"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."}}