{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004610192,0.002015854,0.0009647235,0.002243468,0.0005400407,0.002244123,0.00122754,0.001901496,0.003852284],"category_scores_gemma":[0.005577042,0.001296285,0.0006755573,0.001503618,0.002267301,0.001703943,0.001361104,0.002315189,0.002680267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006918312,"about_ca_system_score_gemma":0.0007348736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004693804,"about_ca_topic_score_gemma":0.001144026,"domain_scores_codex":[0.9924315,0.001879241,0.0004285701,0.00240896,0.002619155,0.0002324071],"domain_scores_gemma":[0.9949463,0.002617126,0.0008273663,0.0005415331,0.0009149092,0.0001526854],"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":[0.00004463347,0.00005388195,0.000547187,0.0003049266,0.00001486766,0.00005328342,0.0001692643,0.000427922,0.9806007,0.0008004836,0.0003145273,0.01666832],"study_design_scores_gemma":[0.000008831406,0.0003193912,0.001538471,0.00007719417,0.00004314171,0.0004265005,0.0001148948,0.006907941,0.9769648,0.0009045977,0.01264072,0.00005349527],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04617824,0.004457616,0.9410155,0.0004792649,0.0004700646,0.0005235015,0.0008576263,0.001722752,0.004295476],"genre_scores_gemma":[0.1357173,0.005271673,0.8438062,0.0007225297,0.0001653515,0.0009940176,0.001320413,0.0004895412,0.01151304],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004610192,"threshold_uncertainty_score":0.02438134,"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."}}