{"id":"W3014485484","doi":"10.1016/j.foodchem.2020.126708","title":"An innovative paper-based device for DNA extraction from processed meat products","year":2020,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Gwangju Institute of Science and Technology; Geomembrane Technologies","keywords":"Mitochondrial DNA; Food science; Animal species; Processed meat; Extraction (chemistry); Biology; DNA extraction; Species identification; Identification (biology); genomic DNA; Bottleneck; Food labeling; Biotechnology; DNA; Polymerase chain reaction; Chemistry; Computer science; Chromatography; Genetics; Gene; Zoology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006815924,0.0001543271,0.0001116669,0.000009968682,0.0001005377,0.00006793559,0.0002371055,0.0001637613,0.00006787929],"category_scores_gemma":[0.0003357532,0.0001678149,0.00004091236,0.0002316694,0.00004496746,0.00001700046,0.0000162175,0.0000840855,0.00001350614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001010546,"about_ca_system_score_gemma":0.0001874885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001535232,"about_ca_topic_score_gemma":0.000002770091,"domain_scores_codex":[0.9988753,0.00001860408,0.0002483095,0.0005804375,0.0001273964,0.0001499567],"domain_scores_gemma":[0.9987127,0.00001245723,0.0001712614,0.0003926595,0.0006178456,0.00009312999],"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":[0.0001495393,0.0001182918,0.00004719565,0.0001495359,0.0000393154,1.309809e-7,0.00008887736,0.00001790574,0.9967956,0.0000166668,0.002216231,0.0003607169],"study_design_scores_gemma":[0.0005338015,0.0001518434,0.0001558403,0.0000080068,0.00001764808,6.223447e-7,0.0001980155,0.0001720822,0.9106433,0.00001110592,0.0879137,0.0001940538],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913493,0.0001370653,0.004693191,0.002619066,0.00009816991,0.0003355198,0.0002201678,0.00006749628,0.0004800292],"genre_scores_gemma":[0.9920274,0.000004097707,0.001733775,0.0009911143,0.0006086418,0.0001402932,0.00428994,0.00003078813,0.000173958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08615232,"threshold_uncertainty_score":0.6843292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04019347890096719,"score_gpt":0.2940993477424411,"score_spread":0.2539058688414739,"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."}}