{"id":"W3003451645","doi":"10.1016/j.fct.2020.111136","title":"Corrigendum to “Safety assessment of miraculin using in silico and in vitro digestibility analyses” [Food Chem. Toxicol. 133 (2019 Nov) 110762]","year":2020,"lang":"en","type":"erratum","venue":"Food and Chemical Toxicology","topic":"Dye analysis and toxicity","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Intertek (Canada)","funders":"","keywords":"In silico; In vitro; Computational biology; Chemistry; Food safety; Food science; Biotechnology; Biochemistry; Biology; 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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.000375392,0.0007452588,0.002425612,0.0002930096,0.00006387999,0.00005021681,0.000499762,0.001755107,0.0003446853],"category_scores_gemma":[0.0004324211,0.0007483819,0.0003200182,0.000762741,0.0002873341,0.00008807002,0.0007542415,0.002029869,0.000002479714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004920445,"about_ca_system_score_gemma":0.0004607592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001418259,"about_ca_topic_score_gemma":0.0004990669,"domain_scores_codex":[0.9954548,0.0001261932,0.001537143,0.001613041,0.0004600804,0.0008087257],"domain_scores_gemma":[0.9979976,0.0001948092,0.0005244903,0.0006757287,0.000110457,0.000496854],"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":[0.0004243328,0.0009046478,0.003326459,0.0007469787,0.0003717142,0.00003857497,0.0001395984,0.00002850018,0.9872078,0.00002872824,0.005973401,0.0008092425],"study_design_scores_gemma":[0.003329813,0.0008122171,0.006822226,0.0004780971,0.0006567995,0.00003881704,0.0003143067,0.007797634,0.9530318,0.0003608841,0.0245758,0.001781627],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893865,0.001273977,0.00004861873,0.0005251473,0.0009975877,0.0003819122,0.0008266076,0.00004348263,0.006516144],"genre_scores_gemma":[0.995694,0.0002167831,0.0008429179,0.0003722493,0.0005218326,0.00004261588,0.0006671294,0.00006542235,0.001576982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03417604,"threshold_uncertainty_score":0.9995408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05303978601534185,"score_gpt":0.3317090519934731,"score_spread":0.2786692659781312,"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."}}