{"id":"W3109964992","doi":"10.3389/fgene.2020.612515","title":"A Distributed Whole Genome Sequencing Benchmark Study","year":2020,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; McGill University; McGill Genome Centre; Ontario Genomics; Canada's Michael Smith Genome Sciences Centre; SickKids Foundation; University of Toronto; Provincial Health Services Authority","funders":"Hospital for Sick Children; Canada Foundation for Innovation; Genome British Columbia; Canadian Institutes of Health Research; Ontario Genomics; Genome Canada; University of Toronto; GlaxoSmithKline","keywords":"Genome; Benchmark (surveying); Computational biology; Whole genome sequencing; Computer science; DNA sequencing; Biology; Genetics; Gene; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007591545,0.0001651302,0.0001788146,0.00003621578,0.00005678506,0.00003135629,0.0002824882,0.00008937511,0.000008870528],"category_scores_gemma":[0.00004787474,0.0001747507,0.0000696456,0.0001642843,0.00004350753,0.000001856857,0.0001690565,0.00008920208,0.000006396941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003807306,"about_ca_system_score_gemma":0.0001167368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006333751,"about_ca_topic_score_gemma":0.00001130349,"domain_scores_codex":[0.9989088,0.00005439039,0.0002350673,0.0004097557,0.0001177765,0.0002741984],"domain_scores_gemma":[0.9994458,0.000002384519,0.00005604047,0.0002813968,0.00004356767,0.0001708262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003079149,0.0004777961,0.592047,0.00007050757,0.0003126692,0.0002617602,0.001945427,0.01965269,0.3521524,0.000006405975,0.02988384,0.002881546],"study_design_scores_gemma":[0.008345342,0.005016791,0.387672,0.00003662592,0.0003446922,0.00003561534,0.02730094,0.01417171,0.02346007,0.0005438551,0.5301448,0.002927653],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854976,0.00348597,0.009794714,0.0001703544,0.0002526085,0.0003729494,0.0002527654,0.00001175426,0.0001612852],"genre_scores_gemma":[0.9948162,0.0001820659,0.003681913,0.0004272833,0.0002211386,0.00002032955,0.0005785239,0.00002628923,0.00004624153],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5002609,"threshold_uncertainty_score":0.7126125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01141862227032207,"score_gpt":0.2202589005690563,"score_spread":0.2088402782987343,"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."}}