Sustainable Autarky of Food-Energy-Water
Bibliographic record
Abstract
Meeting the demand for food, energy, and water to sustain the worldwide growth of urban population is a major challenge. Several recent reports have concluded that one approach to overcome this challenge is to recover and recycle resources within the food-energywater (FEW) nexus in urban settings. Urban wastewaters (UWW) are now being recognized as a resource, rich in nutrients and energy, rather than a waste stream that has to be treated and disposed of at the expense of significant energy input and associated environmental emissions. Reclaiming reusable water, nutrients, and energy from UWWs can contribute to autarky of FEW nexus and render the wastewater management process sustainable and potentially profitable. This paper presents a novel approach to treat UWW with the potential for high recovery of energy, nutrients, and water from UWW for use in food crop production. This approach entails cultivation of energy-rich algal biomass in primary-settled UWW followed by extraction of biocrude and nutrients from the algal biomass by hydrothermal liquefaction. A fraction of the recovered nutrients is recycled to boost biomass production while the rest can be stockpiled for use as fertilizer. Results from a pilot scale field study conducted at a local wastewater treatment plant confirmed that the algal system can achieve >80% removal of organic carbon, ammoniacal-nitrogen, and phosphates in UWW, meeting the respective discharge standards in a single step, with a batch process time of three days.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".