Environmental life cycle assessment of recombinant growth factor production for cultivated meat applications
Bibliographic record
Abstract
Abstract Growth factors are critical components of current serum-supplemented and serum-free media formulations for cultivated meat production. However, growth factors have been excluded, estimated using proxies, or modeled using proprietary data in existing environmental assessments of cultivated meat products. Cell culture media has been identified as a hotspot in such studies, therefore it is important to accurately quantify the environmental impacts of growth factor supplementation. To address this gap, this study applied life cycle assessment (LCA) methodology to comparatively assess the environmental impacts of recombinant growth factor production for cultivated meat applications. Life cycle inventories were developed for four recombinant growth factors (IGF-1, FGF, TGF-ß, and PDGF) produced using a novel bench- scale process. The functional unit of the product output was selected as 1 mg of produced growth factor. The results indicate that recombinant growth factors can have significant environmental impacts within cultivated meat systems, despite being used in very small quantities. For example, the global warming potential of production of 1 mg of IGF-1, FGF, TGF-ß, and PDGF was estimated to be 0.1, 0.04, 0.2 and 0.2 kg CO 2 eq, respectively. Future research should explore the sustainability of producing these growth factors at scale to meet the needs of the expanding cultivated meat industry or identifying alternatives to these growth factors that have a lower impact on the environment. Nomenclature
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".