Two centuries of nitrogen dynamics: Legacy sources and sinks in the Mississippi and Susquehanna River Basins
Bibliographic record
Abstract
Abstract Global flows of reactive nitrogen (N) have increased significantly over the last century in response to agricultural intensification and elevated levels of atmospheric deposition. Despite widespread implementation conservation measures, N concentrations in surface waters are often remaining steady or continuing to increase. Although such lack of response has been attributed to time lags associated with legacy N stores in subsurface reservoirs, it is unclear what the magnitudes of such stores are and how they are partitioned between shallow soil and deeper groundwater reservoirs. Here we have synthesized data to develop a 214 year (1800–2014) trajectory of N inputs to the land surface of the continental U.S. We have concurrently developed a parsimonious, process‐based model, Exploration of Long‐tErM Nutrient Trajectories (ELEMeNT) that pairs this input trajectory with a travel time‐based approach to simulate transport and retention along subsurface pathways. Using the model, we have reconstructed historic nitrate yields at the outlets of two major U.S. watersheds, the Mississippi River Basin (MRB) and Susquehanna River Basin (SRB). Our results show significant N loading above baseline levels in both watersheds before the widespread use of commercial N fertilizers, largely due to the conversion of forest and grassland to row crop agriculture. Model results also allow us to quantify the magnitudes of legacy N in soil and groundwater pools and to highlight the dominance of soil legacies in MRB and groundwater legacies in SRB. Approximately 55% and 18% of the current annual N loads in the MRB and SRB were found to be older than 10 years of age.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".