{"id":"W3093088743","doi":"10.1038/s41587-020-0715-9","title":"Author Correction: Multiplexed droplet single-cell RNA-sequencing using natural genetic variation","year":2020,"lang":"en","type":"article","venue":"Nature Biotechnology","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Multiplexing; Variation (astronomy); Computational biology; RNA; Biology; Genetics; Computer science; Gene; Physics; Telecommunications","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00008188611,0.0002856182,0.0002512436,0.0001045181,0.0001620137,0.00003790366,0.0003430319,0.001867077,0.00001835674],"category_scores_gemma":[0.0003062818,0.0002922181,0.0001326799,0.0003654755,0.000123954,0.000007923127,0.0001134883,0.0009780874,0.00001128045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009644867,"about_ca_system_score_gemma":0.0001202674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004483084,"about_ca_topic_score_gemma":0.00003707461,"domain_scores_codex":[0.9983824,0.00006723644,0.000313858,0.0006911324,0.0001646209,0.0003806749],"domain_scores_gemma":[0.9992434,0.00001753945,0.0001610611,0.000358144,0.0001176687,0.00010218],"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":[0.00011554,0.0000377152,0.0002365941,0.00002365944,0.00003378761,0.00001363703,0.00008866404,0.0003083169,0.9954142,0.0000369119,0.0007481519,0.0029428],"study_design_scores_gemma":[0.0007381049,0.0003883322,0.0002185753,0.00001264277,0.00004411427,0.00006637027,0.00007364286,0.02074027,0.9603311,0.00002214706,0.01701152,0.0003531562],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9646015,0.002405978,0.02680295,0.002334959,0.003160321,0.0003056382,0.00001696482,0.0002174338,0.0001543164],"genre_scores_gemma":[0.9795699,0.00004114848,0.01799334,0.001353431,0.0007092623,0.000005939467,0.00008139011,0.00005180244,0.0001938099],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03508311,"threshold_uncertainty_score":0.999953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01954612278960521,"score_gpt":0.2319599759352707,"score_spread":0.2124138531456654,"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."}}