Methane Single Cell Protein: securing protein supply during global food catastrophes
Bibliographic record
Abstract
A catastrophe such as supervolcanic eruption, asteroid impact or nuclear winter could reduce global food production by 10% or more. Human civilization’s food production system is unprepared to respond to such an event, and current preparedness centers around food stockpiles, an excessively expensive solution given that a global catastrophic risk (GCR) scenario could hamper conventional agriculture for 5 to 10 years. Instead, it is more cost-effective to consider alternative food production techniques requiring little to no sunlight.This study analyses the potential of single-cell protein (SCP) produced from methane (natural gas) as an alternative food source in the case of a catastrophe that considerably blocked sunlight, the most severe food shock scenario. To determine its viability, the following are quantified: global production potential of methane SCP, capital costs, material and energy requirements, ramp-up rates and retail prices. In addition, potential bottlenecks to fast deployment are considered.While providing a higher quality of protein than other alternatives, the production capacity would be slower to ramp up. Based on 24/7 construction of facilities, 7-11% of global protein requirements could be fulfilled at the end of the first year. Results suggest that investment in production ramp up should aim to meet no more than humanity’s minimum protein requirements. Uncertainty remains around the transferability of labor and equipment production, among other key areas. Research on these questions could help reduce the negative impact of potential food-related GCRs.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".