Chesapeake legacies: the importance of legacy nitrogen to improving Chesapeake Bay water quality
Bibliographic record
Abstract
Abstract In the Chesapeake Bay, excess nitrogen (N) from both landscape and atmospheric sources has for decades fueled algal growth, disrupted aquatic ecosystems, and negatively impacted coastal economies. Since the 1980s, Chesapeake Bay Program partners have worked to implement a wide range of measures across the region—from the upgrading of wastewater treatment plants to implementation of farm-level best management practices—to reduce N fluxes to the Bay. Despite widespread implementation of such measures and notable reductions in N inputs, water quality across the region has been slow to improve. Such lack of response has in some cases been attributed to N legacies—accumulations of surplus N in soils and groundwater—that can contribute to time lags between implementation of conservation measures and improvements in water quality. Here, we use the ELEMeNT-N modeling framework to explore the role of legacy N in slowing reductions in N loading to the Bay, and to provide estimates of the time required to meet water quality goals in nine major tributary watersheds. Our results first show that recent improvements in water quality can be attributed to decreases in N surplus magnitudes that began to occur in the 1970s and 1980s, and that such improvements will continue in the coming decades. Future simulations suggest that, even with no additional changes in current management practices, goals to reduce N loads across the region by 25% can nearly be met within the next two decades. The present results also suggest that time lags to achieving water quality may vary considerably in the individual study watersheds, with the longest lag times being found in the highly agricultural Choptank watershed, where N surplus magnitudes remain high and where legacy N remains a major control on water quality.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".