{"id":"W2919782064","doi":"10.1038/s41467-019-08814-w","title":"Publisher Correction: Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set","year":2019,"lang":"en","type":"erratum","venue":"Nature Communications","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of British Columbia; Discovery Centre; University Health Network","funders":"","keywords":"Set (abstract data type); Computer science; Variation (astronomy); Computational biology; Order (exchange); Data set; Library science; Information retrieval; Data science; Biology; Artificial intelligence; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01265609,0.002110623,0.002105034,0.007823421,0.002883242,0.007623147,0.00348307,0.00360934,0.1285447],"category_scores_gemma":[0.2072058,0.00118801,0.001758851,0.01236851,0.001736982,0.002930688,0.003687178,0.007123257,0.05379462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003277844,"about_ca_system_score_gemma":0.008738876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02545701,"about_ca_topic_score_gemma":0.02910147,"domain_scores_codex":[0.9849542,0.002619324,0.002667036,0.002050997,0.007089816,0.0006186703],"domain_scores_gemma":[0.8842067,0.04110209,0.004905468,0.01251479,0.05550664,0.001764393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002942004,0.000006040058,0.0004402361,0.0001603714,0.00003203759,0.0001589156,0.00005936243,0.0001338372,0.00006362983,0.001169151,0.988291,0.009455935],"study_design_scores_gemma":[0.00008046324,0.00002109861,0.003127718,0.0007584569,0.0001550323,0.0009340294,0.0001929906,0.001026359,0.0009387485,0.004604239,0.9880562,0.0001047802],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.001955323,0.002191747,0.01270679,0.07364338,0.8061031,0.000109174,0.08471172,0.004080456,0.01449844],"genre_scores_gemma":[0.08274194,0.01052536,0.05739196,0.06472038,0.09922513,0.0009456371,0.1670929,0.02182546,0.4955312],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1285447,"threshold_uncertainty_score":0.430025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.494172848953352,"score_gpt":0.5928650619301072,"score_spread":0.09869221297675529,"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."}}