{"id":"W1999674531","doi":"10.1038/ng2093","title":"Challenges and standards in integrating surveys of structural variation","year":2007,"lang":"en","type":"review","venue":"Nature Genetics","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":417,"is_retracted":false,"has_abstract":false,"ca_institutions":"SickKids Foundation; MaRS; Hospital for Sick Children","funders":"Wellcome Trust","keywords":"Variation (astronomy); Biology; Structural variation; Data science; Human genetic variation; Computational biology; Copy-number variation; Genetic variation; Genome; Human genome; Genetics; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001142866,0.0002391696,0.0005265895,0.0001168668,0.00002555677,0.00001312346,0.0001545819,0.0009379429,0.000004848349],"category_scores_gemma":[0.0001606604,0.0002024904,0.00009942658,0.0000871434,0.00004166226,0.000001910073,0.0001078267,0.0003719264,1.972554e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005237561,"about_ca_system_score_gemma":0.0002885138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009856535,"about_ca_topic_score_gemma":0.0003822258,"domain_scores_codex":[0.9986477,0.0002085617,0.0004456515,0.0003088204,0.000215864,0.0001733304],"domain_scores_gemma":[0.9992046,0.00003451696,0.0002455316,0.0002498595,0.0002305331,0.00003496764],"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":[0.00000389246,0.000009067314,0.0001102593,0.002555274,0.00006817954,0.000001382563,0.0001837802,0.000003315219,0.00003561049,0.0003579279,0.00001233878,0.996659],"study_design_scores_gemma":[0.0003777329,0.0002627504,0.008737247,0.001737015,0.0001918471,0.00006785469,0.0001938094,0.00001267202,0.0003385259,0.0003106584,0.9872181,0.0005517767],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002967862,0.9955011,0.0001201646,0.000004007634,0.0002065353,0.0002118395,0.000209931,0.000003211962,0.0007753766],"genre_scores_gemma":[0.04382989,0.9542626,0.00140988,0.00000664987,0.0002096659,0.000006753352,0.0002151144,0.00002553407,0.00003388182],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9961072,"threshold_uncertainty_score":0.825732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02669367178938633,"score_gpt":0.3293696474576042,"score_spread":0.3026759756682179,"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."}}