{"id":"W2329888261","doi":"10.1021/jf500610w","title":"Influence of Amount of Starting Material for DNA Extraction on Detection of Low-Level Presence of Genetically Engineered Traits","year":2014,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Genetically Modified Organisms Research","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"DNA extraction; Canola; Extraction (chemistry); DNA; Replicate; Genetically modified organism; Biology; Chromatography; Biotechnology; Polymerase chain reaction; Chemistry; Agronomy; Genetics; Mathematics; Gene; Statistics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.007573032,0.001521333,0.001161792,0.00119058,0.0003674843,0.001711161,0.0009177292,0.001182119,0.001151827],"category_scores_gemma":[0.01478477,0.001155366,0.0008026999,0.0007188264,0.001325301,0.0005224466,0.001272978,0.001162142,0.0005866088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004196911,"about_ca_system_score_gemma":0.0006225082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000716073,"about_ca_topic_score_gemma":0.001934489,"domain_scores_codex":[0.9869632,0.005231748,0.001788088,0.002692054,0.002791638,0.0005333126],"domain_scores_gemma":[0.9784892,0.01427116,0.002511675,0.001831746,0.002477058,0.0004191668],"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":[0.000456571,0.0001553387,0.001586926,0.0001165002,0.00004086995,0.00004016202,0.0001218519,0.00021212,0.9946495,0.00004205525,0.00002141386,0.002556613],"study_design_scores_gemma":[0.0000256794,0.001231901,0.004390203,0.00002132496,0.0001345572,0.0000702858,0.00005447487,0.00107389,0.9922273,0.0000534926,0.0006922616,0.00002465147],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8867684,0.001210676,0.1079668,0.0002273205,0.0001519225,0.001236407,0.000411389,0.0006160083,0.001411197],"genre_scores_gemma":[0.6998676,0.0009580657,0.2919829,0.0004335347,0.00007677214,0.002396492,0.001574947,0.0005062982,0.002203511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007573032,"threshold_uncertainty_score":0.04005051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01546568897439455,"score_gpt":0.2149655645474818,"score_spread":0.1994998755730872,"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."}}