Bibliographic record
Abstract
Meat produced in vitro has been proposed as a humane, safe and environmentally beneficial alternative to slaughtered animal flesh as a source of nutritional muscle tissue. The basic methodology of an in vitro meat production system (IMPS) involves culturing muscle tissue in a liquid medium on a large scale. Each component of the system offers an array of options which are described taking into account recent advances in relevant research. A major advantage of an IMPS is that the conditions are controlled and manipulatable. Limitations discussed include meeting nutritional requirements and large scale operation. The direction of further research and prospects regarding the future of in vitro meat production will be speculated. Industrial relevance: The development of an alternative meat production system is driven by the growing demand for meat and the shrinking resources available to produce it by current methods. Implementation of an in vitro meat production system (IMPS) to complement existing meat production practices creates the opportunity for meat products of different characteristics to be put onto the market. In vitro produced meat products resembling the processed and comminuted meat products of today will be sooner to develop than those resembling traditional cuts of meat. While widening the scope of the meat industry in practices and products, the IMPS will reduce the need for agricultural resources to produce meat.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".