{"id":"W2491999693","doi":"10.1007/978-1-61779-089-8","title":"High-Throughput Next Generation Sequencing","year":2011,"lang":"en","type":"book","venue":"Methods in molecular biology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agriculture and Agri-Food Canada; College of Veterinary Medicine, University of Georgia; Public Health Agency of Canada; University of California, Los Angeles; Biodesign Institute, Arizona State University; Public Health Agency; Technische Universität Bergakademie Freiberg; Institute of Genetics; Arizona State University","keywords":"Throughput; DNA sequencing; Computer science; Computational biology; Biology; Genetics; Telecommunications; DNA","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0008147787,0.0004908297,0.0006000023,0.0001914934,0.00005462685,0.00003164739,0.0004559486,0.001376963,0.00009559406],"category_scores_gemma":[0.000399891,0.0005341123,0.0002330341,0.00008166888,0.0002036117,0.000002581691,0.0004009742,0.000431301,0.00002429992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000256551,"about_ca_system_score_gemma":0.0008218247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002037007,"about_ca_topic_score_gemma":0.0002038625,"domain_scores_codex":[0.9972637,0.0005395732,0.0005936466,0.001038,0.00008161795,0.0004834904],"domain_scores_gemma":[0.9984997,0.00006416941,0.000291738,0.000928505,0.0001166417,0.00009926944],"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":[0.00002737692,0.00002096434,0.00001830391,0.00002703169,0.000105125,0.00003790101,0.00003420993,0.000137385,0.9623973,0.01627638,0.004081356,0.01683669],"study_design_scores_gemma":[0.0006019765,0.0005180773,0.000005734897,0.00004236116,0.0000903691,0.00003730376,0.000009106947,0.0001248627,0.5535635,0.03569039,0.4084563,0.0008599754],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001878998,0.01153634,0.9333091,0.00005624987,0.002042581,0.0005510185,0.0001100146,0.00002362947,0.05049208],"genre_scores_gemma":[0.0004430657,0.00353527,0.9414453,0.001902474,0.001661141,0.0001277392,0.00275992,0.0001823288,0.04794278],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4088337,"threshold_uncertainty_score":0.9999195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06973405407109323,"score_gpt":0.3570600971071708,"score_spread":0.2873260430360776,"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."}}