Leaching the good stuff: nitrogen and phosphorus in real and experimental urban agricultural settings
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
Abstract Urban agriculture provides multiple services, but its potentially adverse impact on water quality remains poorly understood. Here we present empirical data from three coordinated studies examining nitrogen and phosphorus leaching in Minneapolis-St. Paul, Minnesota, USA, and Linköping Sweden, ranging from controlled experiments to observational studies of garden practices. Using zero-tension lysimeters at 30 cm in the soil over multiple growing seasons, we found that although plots receiving nutrient inputs tended to leach more nutrients than those that did not, annual nutrient input rates were not systematically strong predictors of growing season leaching. Legacy effects from previous soil management and cumulative inputs help explain patterns observed for phosphorus leaching, with differences among treatments becoming visible over time. While urban agriculture can support nutrient circularity through organic waste recycling, careful management is needed to balance this benefit against leaching risks. Long-term monitoring is essential for understanding and managing nutrient losses from such systems.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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