Checkered landscapes: hydrologic and biogeochemical nitrogen legacies along the river continuum
Bibliographic record
Abstract
Abstract Decades of agricultural intensification have led to elevated stream nitrogen (N) concentrations and eutrophication of inland and coastal waters. Despite widespread implementation of a range of strategies to reduce N export, expected improvements in water quality have not been observed. This lack of success has often been attributed to the existence of legacy N stores within the landscape. Here, we use the ELEMeNT-N model to quantify legacy accumulation and depletion dynamics over the last century (1930–2016) across 14 nested basins within the Grand River Watershed, a 6800 km 2 agricultural watershed in the Lake Erie Basin. Model results reveal significant legacy N accumulation across the basin, ranging from 705 to 1071 kg ha −1 , creating a checkered landscape of N legacies. The largest proportion (82%–96%) of this accumulated N is stored in soil organic N reservoirs, as biogeochemical legacy, and the remaining in groundwater, as hydrologic legacy. The fraction of N surplus accumulated in soil and groundwater is most strongly correlated with the calibrated watershed mean travel time µ , with the accumulation increasing with increases in µ . The mean travel time ranges from 5 to 34 years across the watersheds studied, and increases with increase in tile drainage, highlighting the strong control of anthropogenic management on legacy accumulation. Water quality improvement timescales were found to be heterogeneous across the watersheds, with greater legacies contributing to slower recovery.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".