{"id":"W1994624068","doi":"10.1101/gr.093955.109","title":"Quantitative phenotyping via deep barcode sequencing","year":2009,"lang":"en","type":"article","venue":"Genome Research","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":332,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Human Genome Research Institute; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Biology; Barcode; Deep sequencing; Computational biology; DNA sequencing; Genomics; Cancer genome sequencing; Genome; Personal genomics; Multiplex; Exome sequencing; Whole genome sequencing; Genetics; Massive parallel sequencing; Gene; Computer science; Mutation","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":[0.0006338672,0.0001311832,0.0001404682,0.0001013827,0.0003128292,0.00003845188,0.0002886375,0.00008827104,0.00002910192],"category_scores_gemma":[0.0001161992,0.0001281063,0.00006687371,0.0002031403,0.0001208265,7.490432e-7,0.000167392,0.0001802764,0.00005595728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005816387,"about_ca_system_score_gemma":0.0001097794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003639116,"about_ca_topic_score_gemma":0.00003716417,"domain_scores_codex":[0.998462,0.0001411649,0.0001732712,0.0004064646,0.0002439243,0.000573186],"domain_scores_gemma":[0.9992187,0.00003430083,0.00003112675,0.0003511049,0.0002537251,0.0001110495],"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":[0.00005472895,0.00001948366,0.0001196797,0.000007822432,0.00003980874,0.000005071046,0.0003499513,0.0004383285,0.9908158,0.0007183533,0.00007042772,0.007360609],"study_design_scores_gemma":[0.00327877,0.00893937,0.0968341,0.00008593466,0.00007338383,0.0001342002,0.008062305,0.0102222,0.6311507,0.05141577,0.187047,0.002756209],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9761204,0.007942749,0.006588896,0.000389909,0.00006010348,0.0002651417,0.00001430796,0.00000465841,0.00861388],"genre_scores_gemma":[0.9936851,0.000733578,0.004777557,0.0001628185,0.0002378969,0.00001571256,0.00002519873,0.00001761325,0.000344543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.359665,"threshold_uncertainty_score":0.5224025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07971096724446586,"score_gpt":0.3630725983264532,"score_spread":0.2833616310819873,"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."}}