{"id":"W4386771921","doi":"10.1186/s40538-023-00467-8","title":"Correction: UPLC–ESI–QTOF–MS profiling, antioxidant, antidiabetic, antibacterial, anti-inflammatory, antiproliferative activities and in silico molecular docking analysis of Barleria strigosa","year":2023,"lang":"en","type":"article","venue":"Chemical and Biological Technologies in Agriculture","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"In silico; Profiling (computer programming); Plant biochemistry; Docking (animal); Chemistry; Computational biology; Traditional medicine; Biology; Biochemistry; Medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001665043,0.001555986,0.00159018,0.002417703,0.001185648,0.002000988,0.0022173,0.003454487,0.0415654],"category_scores_gemma":[0.02497011,0.0006831341,0.001054464,0.001508666,0.001240648,0.001362508,0.001805477,0.004043069,0.0227783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001399844,"about_ca_system_score_gemma":0.002515398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004781124,"about_ca_topic_score_gemma":0.004781385,"domain_scores_codex":[0.9981888,0.0002132141,0.0003393046,0.000349759,0.0007180928,0.000190906],"domain_scores_gemma":[0.9834356,0.002308646,0.001340264,0.001351072,0.01063176,0.0009327407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003084557,0.0000272297,0.0006440053,0.000977264,0.00008696153,0.002905906,0.0001505179,0.0003068361,0.002861843,0.001444324,0.9576651,0.03262156],"study_design_scores_gemma":[0.00007185515,0.00007462154,0.002865282,0.0003278032,0.0001222935,0.004035859,0.0001844403,0.0008765167,0.004389065,0.001396775,0.9855861,0.00006942192],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.003161256,0.003686561,0.005226481,0.04027504,0.9395506,0.00007235662,0.003725806,0.001169049,0.003132891],"genre_scores_gemma":[0.1614219,0.01840062,0.0315234,0.0639751,0.2944179,0.0004002588,0.01409738,0.004018679,0.4117447],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0415654,"threshold_uncertainty_score":0.1390501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01305325411570296,"score_gpt":0.2582636327420514,"score_spread":0.2452103786263485,"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."}}