Uses of Cellular Agriculture in Plant-Based Meat Analogues for Improved Palatability
Bibliographic record
Abstract
With a growing population that is expected to double meat consumption in the next decades, more sustainable and affordable proteins need to be developed. Conventional meat production accounts for a considerable amount of greenhouse gas emission, land and water usage, and energy consumption. Plant-based meat alternatives have been a cornerstone in the alternative protein market. In recent years, biomimicry of traditional meat products is the focus on the market. Animal-raised meat has still maintained its popularity as plant-based meat analogues (PBMA) fail to mimic or be better than conventional meat production. PBMA aims to replicate the aesthetic and chemical characteristics of a type of meat without the need of raising animals. Another alternative is the novel cultured meat or “lab-grown meat” that could provide a high protein source. Considerable developments are still needed to produce complex cultured meat products. Because of difficulties of replicating meat proteins in PBMA, a proposition is to use cultured meat components in PBMA. We review the potential use of cellular agriculture in different facets of PBMA for improved sensorial attributes. There is a significant need for research, innovation, and regulation in this field to create an improved product that has a lower impact on the environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".