In addition, o1-preview included a breakdown of metabolites, justifying its new Serine (No. 118) forecast: "Increased levels from targeted supplementation (Sukwattananipaat et al.) and glycan profile optimization (Kranjc et al.)", including several other metabolite forecast explanations with expected breakthroughs. Lastly, o1-preview's summary included author names supporting its forecasts, which included "Sukwattananipaat et al.: Dynamic glucose feed control and targeted amino acid supplementation to improve metabolite levels like arginine, aspartic acid, methionine, serine, glutamine, glucose, and lactate.", and "Kranjc et al.: Glycosylation modulation through feed additives, affecting methionine, serine, and other glycan-related metabolites." 3.5 Sonnet's response with an overall score of 8.5/10 was found to have some incorrect original values, but cited two of the authors' works: "1. Metabolic improvements from Sukwattananipaat and Zhang 2. Lactate metabolism insights from Okamura and Bokelmann 3. Glycosylation control from Kranjc and Liang 4. Temperature and aggregation control from Jones 5. Process optimization from Yao". 3 Opus had a larger number of forecasts at 73 metabolites with accurate original values, but was repetitive in author citations referencing only Sukwattananipaat. ChatGPT-4o referenced 15 metabolites that were inaccurate and provided only the table. L3.1-405B was able to produce 102 entries, had some inaccurate values, and included extra table headings from CHL-YN-IgG1 (cell pool), but had a good variety of author citations. Herm-405B slowly created a 58 metabolite table, but only addressed 1 of the 4 kinetic time points for Prompt IIb, and was not able to yield generation IIc due to a website auto-refresh after 5 minutes of progress with overall score of "n/a" in Table , Figure . 13 o1-preview Generation IIc: Extracellular Metabolites Supported by Generation IIa Summary, 11 Additional Summaries Certainly! Below is the revised CHL-YN-IgG1 (B-08) rep1 CSV table, including all 136 extracellular metabolites. For each metabolite, I have provided the original numerical values from the CHL-YN-IgG1 (B-08) rep1 dataset, followed by forecasted values based on the methods described in the attached summaries. The authors whose methods are applied are identified in another column, and it is indicated whether a specific breakthrough is expected with the use of the LLM or other authors' methods. Note: The forecasted values are hypothetical and based on the potential improvements suggested in the summaries. They are for illustrative purposes to show how the methods might impact metabolite levels. You can copy the table into a CSV file for download. Revised CHL-YN-IgG1 (B-08) rep1 Metabolite Data [Manuscript Author: See