{"id":"W2943873748","doi":"10.1016/j.foodchem.2019.05.029","title":"Digital PCR as an effective tool for GMO quantification in complex matrices","year":2019,"lang":"en","type":"article","venue":"Food Chemistry","topic":"Genetically Modified Organisms Research","field":"Agricultural and Biological Sciences","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Regional Development Fund; Urad Republike Slovenije za Meroslovje; Thermo Fisher Scientific; Javna Agencija za Raziskovalno Dejavnost RS; Ministry of Agriculture - Saskatchewan","keywords":"Digital polymerase chain reaction; Genetically modified organism; Computational biology; Real-time polymerase chain reaction; Polymerase chain reaction; DNA; Biology; Complex matrix; Chemistry; Biotechnology; Molecular biology; Gene; Food science; Chromatography; Genetics","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":[0.0001036856,0.00008900117,0.0001052123,0.000004735454,0.00004344589,0.0001197489,0.0002517952,0.00009707291,0.0003853317],"category_scores_gemma":[0.000079282,0.00004116394,0.0000416765,0.0001508723,0.00002816164,0.00009559598,0.00005138079,0.00008015395,0.000136373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002854775,"about_ca_system_score_gemma":0.000007436734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001432967,"about_ca_topic_score_gemma":0.0000135895,"domain_scores_codex":[0.9991716,0.00001440632,0.0001248492,0.0003036928,0.0001637456,0.0002216958],"domain_scores_gemma":[0.9995989,0.0001686704,0.0000339054,0.00007824964,0.00005905429,0.00006121493],"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":[0.00004418476,0.00008859752,0.002717108,0.00003038812,0.000004365767,3.029899e-7,0.0000163984,0.000004099166,0.9659386,0.00004387692,0.00004776676,0.03106426],"study_design_scores_gemma":[0.0002709858,0.0006070692,0.1369679,0.000008935823,0.000002622907,0.000003556259,0.000237825,0.0001959417,0.8572942,0.0009457825,0.003283438,0.0001817123],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964003,0.00002083016,0.000005679484,0.0002059782,0.00001494589,0.0004560115,0.00008790757,0.000031772,0.002776591],"genre_scores_gemma":[0.9989731,0.000002195492,0.00007195986,0.00001772046,0.00008301311,0.00005561902,0.0003306187,0.000001357606,0.0004643788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1342508,"threshold_uncertainty_score":0.4219112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02894776127461796,"score_gpt":0.2645947826505822,"score_spread":0.2356470213759642,"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."}}