Production and performance of bio-based mineral fertilizers from agricultural waste using ammonia (stripping-)scrubbing technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Development and optimization of nutrient recovery technologies for agricultural waste is on the rise. The full scale adoption of these technologies is however hindered by complex legal aspects that result from lack of science-based knowledge on characterization and fertilizer performance of recovered end-products. Ammonium sulfate (AS) and ammonium nitrate (AN), end-products of (stripping-)scrubbing technology, are currently listed by the European Commission as high priority products with the potential of replacing synthetic N fertilizers. The legal acceptance of AS and AN will be highly dependent on critical mass of scientific evidence. This study describes four different (stripping-)scrubbing pathways to recover ammonia with an aim to (i) assess product characteristics of ammonium nitrate (AN) and ammonium sulfate (AS) produced from different installations, (ii) evaluate fertilizer performance of recovered end-products in greenhouse (Lactuca sativa L.) and full field (Zea mays L.) scale settings and (iii) compare the observed performances with other published studies. Results have indicated that the recovered products might have a different legal status, as either mineral N fertilizer or yet as animal manure, depending on the used (stripping-)scrubbing process pathway. Nevertheless, no significant differences in respect to product characterization and fertilizer performance of AN and AS have been identified in this study as compared to the conventional use of synthetic N fertilizers. This indicates that recovered AS and AN are valuable N sources and therefore might be used as N fertilizers in crop cultivation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it