Bibliographic record
Abstract
Interest and technological know-how in cell-based meat production has grown tremendously in recent years. The appeal is wide ranging, but two main drivers include: i) the possibility of producing edible meat without requiring the slaughter of sentient animals; and ii) the potential to significantly reduce the environmental impact of animal agriculture. Owing to these potential benefits, proponents have called for major government investments in cell-based meat to further develop the technology and help launch the industry. This article critically examines the environmental promise of cell-based meat, focussing specifically on its potential role in climate change mitigation, and specifically within the context of Canada’s agri-food sector. The analysis is founded upon a comparison of available life cycle greenhouse gas assessments of cell-based and conventional meat, supplemented with contextual data about the Canadian agri-food sector. Cell-based meat in Canada is found to have a likely carbon footprint similar in scale to poultry meat, pork, and beef from dairy cattle, though considerably lower than meat from beef cattle. Alongside these findings and additional contextual factors pertaining to Canada’s agri-food sector, the paper argues that cell-based meat is best understood as one tool among many which could potentially support greenhouse gas emissions reductions in domestic food production if supporting conditions are met, not a silver bullet climate solution obtained by fully replacing conventional meat.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".