Current status of research and market in alternative protein
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
As the global population increases and issues regarding health, environment, and animal welfare emerge, interest in alternative proteins is rising along with the emphasis on sustainability and food security in agriculture and livestock. Based on protein sources, alternative proteins can be divided into plant-based meat, animal cell-cultured meat, and edible insect. Alternative meat market will keep growing and accounting for 11% of the total protein food market by 2035. America has the largest share in the alternative protein market. Many food companies and startups are developing and distributing alternative proteins in Korea which is ranked 38th. Among them, plant based meat shows advantages in terms of production cost and safety verification, but may present some issues that include anti-nutrients and allergens. Animal-derived cell cultured meat can best mimic traditional meat products, but may have concerns for food safety and high production cost. In order to shift from traditional animal based meat production into extraction-, fermentation-, or culture-based alternative protein manufacturing system, it is necessary to understand the origins, pros and cons, and the current status of research and market for better forecast their future promises and challenges.
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