{"id":"W1998866746","doi":"10.1007/s00216-009-3295-6","title":"Detection and identification of multiple genetically modified events using DNA insert fingerprinting","year":2009,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Genetically Modified Organisms Research","field":"Agricultural and Biological Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Cegep de Sainte Foy; Agriculture and Agri-Food Canada; Canadian Food Inspection Agency","funders":"Canadian Food Inspection Agency","keywords":"DNA profiling; Insert (composites); Polymerase chain reaction; Fingerprint (computing); DNA; Computational biology; Genetically modified organism; Capillary electrophoresis; Genetically modified maize; Biology; Restriction enzyme; Molecular biology; Genetics; Transgene; Genetically modified crops; Gene; Computer science","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":[0.0005637194,0.0004770556,0.000417744,0.001065884,0.0002473996,0.0005451236,0.0005333842,0.000960815,0.0007991224],"category_scores_gemma":[0.001895022,0.0003248097,0.0004128725,0.0006331758,0.0004052922,0.0006090721,0.0004814672,0.001033122,0.0005758176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002132725,"about_ca_system_score_gemma":0.0001965219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005520689,"about_ca_topic_score_gemma":0.0008584575,"domain_scores_codex":[0.9993243,0.00009687795,0.00004433028,0.0002610275,0.0001753184,0.00009812392],"domain_scores_gemma":[0.9983228,0.0006762436,0.0004141086,0.0001910078,0.0002283573,0.000167401],"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":[0.0002354767,0.00007544664,0.003088376,0.00003734562,0.000015588,0.0001002353,0.00005485427,0.0001482623,0.988305,0.0001725343,0.00002917759,0.007737717],"study_design_scores_gemma":[0.000009864509,0.0003097343,0.0111454,0.00001142559,0.00006427978,0.0005692642,0.00005956372,0.002303214,0.9845681,0.0001788275,0.0007621932,0.00001813653],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8956268,0.000890296,0.09994997,0.0001312146,0.00007149517,0.0001373567,0.0009198756,0.0003815148,0.001891335],"genre_scores_gemma":[0.8836669,0.0005887245,0.112408,0.0001057356,0.00001569832,0.0001052487,0.001014929,0.00004498362,0.002049738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001065884,"threshold_uncertainty_score":0.002981305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02849486012114794,"score_gpt":0.2537440718715721,"score_spread":0.2252492117504241,"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."}}