Bibliographic record
Abstract
This review details the core activity in cellular agriculture conducted in the UK at the end of 2019, based upon a literature review by, and community contacts of the authors. Cellular agriculture is an emergent field in which agricultural products—most typically animal-derived agricultural products—are produced through processes operating at the cellular level, as opposed to (typically farm-based) processes operating at the whole organism level. Figurehead example technologies include meat, leather and milk products manufactured from a cellular level. Cellular agriculture can be divided into two forms: ‘tissue-engineering based cellular agriculture’ and ‘fermentation-based cellular agriculture’. Products under development in this category are typically valued for their environmental, ethical, and sometimes health and safety advantages over the animal-derived versions. There are university laboratories actively pursuing research on meat products through cellular agriculture at the universities of Bath, Newcastle, Aberystwyth, and Aston University in Birmingham. A cellular agriculture approach to producing leather is being pursued at the University of Manchester, and work seeking to produce a palm oil substitute is being conducted at the University of Bath. The UK cellular agriculture companies working in the meat space are Higher Steaks, Cellular Agriculture Ltd, CellulaRevolution, Multus Media and Biomimetic Solutions. UK private investors include CPT Capital, Agronomics Ltd, Atomico, Backed VCs, and Breakoff Capital. The UK also has a strong portfolio of social science research into diverse aspects of cellular agriculture, with at least ten separate projects being pursued over the previous decade. Three analyses of the environmental impact of potential cellular agriculture systems have been conducted in the UK. The first dedicated third-sector group in this sector in the UK is Cultivate (who produced this report) followed by Cellular Agriculture UK. International groups New Harvest and the Good Food Institute also have a UK presence.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".