Synergistic pathways to a circular bionutrient economy
Bibliographic record
Abstract
Bionutrient circularity can increase food system sustainability. Global food production currently depends substantially on synthetic fertilizers, while massive volumes of crop residues, food scraps, and excreta are undervalued and mismanaged, contributing to environmental degradation and climate change. Transforming these organic underutilized resources through combinations of physiochemical, biological, and thermochemical processes can improve public hygiene while keeping carbon and nutrients within the food system. By redirecting both organic matter and nutrients to soils, bionutrient circularity can offset fertilizer and energy costs. Meanwhile, circular feeds can enable livestock sectors to grow without increasing land demands for crop production, much of which is currently fed to livestock. Synergistic integration of transformation processes and resource recovery pathways will unlock substantial economic and environmental benefits. Realizing the potential of a circular bionutrient economy, however, will require robust management of contaminants, navigation of context-dependent tradeoffs, and integration of sociocultural, technical, operational and regulatory innovation processes. Multiple transformation processes can be utilized to convert organic underutilized resources into products and ecosystem services in a circular bionutrient economy. Synergies among these processes can lead to viable pathways for reducing waste, keeping nutrients in circulation and regenerating nature by enhancing soil health, offsetting the greenhouse gas emissions associated with synthetic nitrogen production and use, alleviating pressure on land, fisheries (current sources of nutrients for animal feeds) and finite stocks of phosphorus, and reducing nutrient pollution. • Recycling nutrients and carbon from by-products to the food system can reduce pollution. • Biological, thermal, and physical transformation processes can be synergistic. • Biochar enhances many biological transformations. • Social, entrepreneurial and policy shifts are needed to support bionutrient circularity.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".