{"id":"W4408534952","doi":"10.1079/ab.2025.0024","title":"Managing regulatory issues arising from new diagnostic technologies: High throughput sequencing as a case study","year":2025,"lang":"en","type":"article","venue":"CABI Agriculture and Bioscience","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Canada; Canadian Food Inspection Agency","funders":"","keywords":"Throughput; DNA sequencing; Computational biology; Computer science; Biology; Genetics; Telecommunications; Gene","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":[0.04066216,0.0008817128,0.0005456159,0.001481665,0.007354218,0.005890008,0.0034055,0.008263395,0.002533882],"category_scores_gemma":[0.03310356,0.0004314528,0.0009648404,0.001832878,0.004188601,0.003349255,0.003245854,0.004963182,0.0006336863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005915868,"about_ca_system_score_gemma":0.006933609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007827302,"about_ca_topic_score_gemma":0.01809864,"domain_scores_codex":[0.9715025,0.02069555,0.0007348185,0.0009052146,0.004721517,0.001440416],"domain_scores_gemma":[0.9446654,0.04071527,0.003018581,0.002052142,0.006057365,0.003491282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001404576,0.006204269,0.05862687,0.0028194,0.0002666462,0.2079865,0.0875348,0.03826762,0.02753799,0.1197095,0.06471411,0.3849278],"study_design_scores_gemma":[0.0003917562,0.005188406,0.02040725,0.002574789,0.0003084505,0.09899034,0.1330674,0.06024409,0.05033183,0.07396521,0.5539742,0.0005562927],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6428735,0.006034302,0.154176,0.1173376,0.001299961,0.002955815,0.0004107601,0.0005603638,0.07435174],"genre_scores_gemma":[0.777672,0.006026014,0.1876963,0.01306918,0.0005029416,0.000933491,0.0003021077,0.0002425472,0.0135554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04066216,"threshold_uncertainty_score":0.2150446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005844315635480461,"score_gpt":0.2783570485248283,"score_spread":0.2725127328893479,"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."}}