An investigation of the formulation and nutritional composition of modern meat analogue products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Meat analogues, or plant-based products that simulate the properties of traditional meat products, have secured a position in the conversation of protein foods. Rapid growth of the meat analogue industry is occurring in the global food marketplace in both the retail and food service sectors. The purpose of this review was to investigate the ingredients used in the formulation of modern meat analogues, evaluate the nutrient specifications of modern meat analogue products, and then form a comparison with traditional meat products. Based on this investigation, it was determined – firstly, the ingredients used in the formulation of modern meat analogue products make these products fit under the classification of ultra-processed foods; and secondly, the nutrient specifications of popular meat analogue products can effectively simulate the nutrient specifications of the meat products they are attempting to simulate. Therefore, based on these findings, modern meat analogue products can offer roughly the same composition of nutrients as traditional meat products, albeit with many different ingredients and a high level of further processing. Keywords: Plant-based meat, Simulated meat, Meat alternatives, Processed foods, Protein foods
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it