Egestabase – An online evidence platform to discover and explore options to recover plant nutrients from human excreta and domestic wastewater for reuse in agriculture
Bibliographic record
Abstract
Restoring nutrient circularity across scales is important for ecosystem integrity as well as nutrient and food security. As such, research and development of technologies to recover plant nutrients from various organic residues has intensified. Yet, this emerging field is diverse and difficult to navigate, especially for newcomers. As an increasing number of actors search for circular solutions to nutrient management, there is a need to simplify access to the latest knowledge. Since the majority of nutrients entering urban areas end up in human excreta, we have chosen to focus on human excreta and domestic wastewater. Through systematic mapping with stakeholder engagement, we compiled and consolidated available evidence from research and practice. In this paper, we present 'Egestabase' - a carefully curated open-access online evidence platform that presents this evidence base in a systematic and accessible manner. We hope that this online evidence platform helps a variety of actors to navigate evidence on circular nutrient solutions for human excreta and domestic wastewater with ease and keep track of new findings.
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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 | 0.046 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.021 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.061 | 0.011 |
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".