A proposed framework for evaluating meat alternatives
Bibliographic record
Abstract
Abstract Concerns surrounding the environmental, economic, and ethical consequences of meat production and industrial agriculture have prompted substantial research and capital investment into the production of meat alternatives. Alternative meat production encompasses a variety of technological approaches including plant-based meats, cell-based or cultivated meats, meat alternatives relying on fungal protein sources, and hybrids thereof; each of which offers unique advantages and disadvantages and has been associated with a myriad of claims supporting it as the preferred alternative to animal-derived meats. As part of XPRIZE Foundation’s Feed the Next Billion competition, we developed a framework for evaluating meat alternatives by measuring their structural, nutritional, and organoleptic properties while also assessing safety and their purported environmental and economic benefits compared to animal-derived meats. The framework is technologically agnostic and can be used to evaluate meat alternatives of all types. The output of the framework enables a data-driven comparison to animal-derived meat and/or other alternative meats, allowing a range of stakeholders (e.g., food startups, investors, government) to assess technological readiness, competitive advantage, and impact potential. This framework can assist this nascent industry as it moves towards standardizing approaches to evaluating the quality, safety and proposed benefits of meat alternatives.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".