{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000309303,0.0001731088,0.0002675909,0.00001890214,0.0001355553,0.00006092922,0.0001641476,0.0002028915,0.00006913218],"category_scores_gemma":[0.0004615996,0.00008768447,0.00008409149,0.0003347615,0.0002225798,0.00005724472,0.0001157308,0.0001923733,0.00000288556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002329905,"about_ca_system_score_gemma":0.00001203964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005061513,"about_ca_topic_score_gemma":0.00001374898,"domain_scores_codex":[0.9983208,0.00003910127,0.0004769175,0.0004658041,0.0003546454,0.0003427581],"domain_scores_gemma":[0.9991938,0.0002077246,0.00008337387,0.00009805171,0.0001329383,0.0002840793],"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":[0.00004089334,0.00009826628,0.003085625,0.00002280763,0.00001916044,0.000002050247,0.000006427093,0.0000073908,0.9593099,0.00009944769,8.47502e-7,0.03730722],"study_design_scores_gemma":[0.0002495548,0.0001386056,0.3716955,0.00002524686,0.00008959423,0.00002298086,0.00005625922,0.132703,0.4925055,0.00223003,0.00002964246,0.000254078],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984567,0.0000736146,0.0003850442,0.000700438,0.00000867291,0.0001027599,0.000009195943,0.00002498555,0.0002385631],"genre_scores_gemma":[0.9995039,0.00004816108,0.0002136127,0.00005882995,0.00008339604,0.000001411738,0.00000707771,0.000001617597,0.00008207191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4668044,"threshold_uncertainty_score":0.3575669,"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."}}