Nitrogen legacies in anthropogenic landscapes: a case study in the Mondego Basin in Portugal
Bibliographic record
Abstract
Abstract Nitrogen (N) legacies have built up in anthropogenic landscapes over decades of agricultural intensification, and these legacies lead to time lags in water quality change measurable even beyond the moment of application of N. It is important to understand these legacies to quantify the relationship between N inputs and N concentrations in streams and implement best management practices for water quality improvement; however, little is known about the magnitude of legacies in various landscape elements like soils and groundwater. Here, we have used the ELEMeNT (Exploration of Long-tErM Nutrient Trajectories) model to explore the buildup and depletion of N legacies over a 216-year period, across the Mondego River Basin, a 6645-km 2 watershed in Portugal, where human interventions have considerably changed the characteristics of the basin to prevent floods and improve farming conditions in recent decades. The results show that the increase in the amount of inorganic fertilizer applied was the main driver for the anthropogenic N loads in the watershed from 1950 until the beginning of the 1990s. The N inputs have been decreasing since then, but N loads in the river did not document any decrease till the 1990s; after which there was a decline. This time lag between the N inputs to the watershed and the N loads in the river (about two decades) is a function of accumulation of N legacy.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".