How we can mine asteroids for space food
Bibliographic record
Abstract
Abstract To deeply explore the solar system, it will be necessary to become less reliant on the resupply tether to Earth. An approach explored in this study is to convert hydrocarbons in asteroids to human edible food. After comparing the experimental pyrolysis breakdown products, which were able to be converted to biomass using a consortia, it was hypothesized that equivalent chemicals found on asteroids could also be converted to biomass with the same nutritional content as the pyrolyzed products. This study is a mathematical exercise that explores the potential food yield that could be produced from these methodologies. This study uses the abundance of aliphatic hydrocarbons in the Murchison meteorite (>35 ppm) as a baseline for the calculations, representing the minimum amount of organic matter that could theoretically be attributed to biomass production. Calculations for the total carbon in solvent-insoluble organic matter (IOM) represent the maximum amount of organic matter that could theoretically be attributed to food production. These two values will provide a range of realistic yields to determine how much food could theoretically be extractable from an asteroid. The results of this study found that if only the aliphatic hydrocarbons can be converted into biomass (minimum scenario) the resulting mass of edible biomass extractable from asteroid Bennu ranges from 5.070 × 107 g to 2.390 × 108 g. If the biomass extraction process, however, is more efficient, and all IOM is converted into edible biomass (maximum scenario), then the mass of edible biomass extractable from asteroid Bennu ranges from 1.391 × 109 g to 6.556 × 109 g. This would provide between 5.762 × 108 and 1.581 × 1010 calories that is enough to support between 600 and 17 000 astronaut life years. The asteroid mass needed to support one astronaut for one year is between 160 000 metric tons and 5000 metric tons. Based on these results, this approach of using carbon in asteroids to provide a distributed food source for humans appears promising, but there are substantial areas of future work.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".