Rapid repurposing of pulp and paper mills, biorefineries, and breweries for lignocellulosic sugar production in global food catastrophes
Bibliographic record
Abstract
Producing sugar from lignocellulosic biomass is a promising resilient food solution to counter the near-total global failure of food production due to the agricultural collapse that would likely follow an abrupt sunlight reduction catastrophe such as a nuclear winter, a supervolcanic eruption, or a large asteroid or comet impact.This study examines how quickly edible sugar production could be ramped up globally by repurposing pulp and paper mills, sugarcane biorefineries, corn biorefineries, and breweries for lignocellulosic sugar production. A sub-unit component comparison to the NREL 2017 Biochemical Sugar Model indicates that 61%, 62%, 85% and 38% of ISBL unit components are present, respectively. Fast construction methods were studied to analyze how this and other industrial foods could be rapidly leveraged in a catastrophe.Results suggest that the world’s current sugar demand could quickly be fulfilled by repurposing pulp and paper mills for lignocellulosic sugar production, given 5 months of production ramp-up and 24/7 construction. This method could reduce construction time to an estimated 32% of the original at an increased labor cost of 1.47 times, resulting in sugar production beginning 5 months after the catastrophe at a retail cost of $0.82 USD/kg. This could not only contribute a significant share of the food requirement after the catastrophe (∼28% within the first year), but also be key to preventing global starvation between the time at which global food storages run dry and other resilient food solutions can scale up significantly.This study aims to serve as the basis for more comprehensive scenario analyses. More research is needed to characterize material and labor constraints to fast response in more depth; repurposing and fast construction pilot studies and food safety studies are recommended.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".