Spatio-Temporal Patterns in Net Anthropogenic Nitrogen and Phosphorus Inputs Across the Grand River Watershed
Bibliographic record
Abstract
Over the last century, human activities have dramatically increased the inputs of nitrogen (N) and phosphorus (P) to land, resulting in increased eutrophication of aquatic systems, and degradation of drinking water quality. Although many changes in management have been adopted to mitigate these impacts, little improvement has been observed in water quality. Multiple N and P mass balance studies have indicated imbalances between inputs and outputs of N and P in anthropogenic landscapes. In this work, historical (1901-2011) N and P budgets for the Grand River Watershed (GRW) in southwestern Ontario were developed using the NANI/NAPI (net anthropogenic N/P input) framework. NANI was calculated as the sum of four different components: commercial fertilizer N application, atmospheric N deposition, net food and feed imports, and biological N fixation. A similar budgeting method was used to estimate NAPI, which includes fertilizer P application, net food and feed imports and detergent P use by humans. Relevant data was obtained from the Canadian agricultural census, Environment Canada, and literature estimates. Our results showed that annual NANI and NAPI values increased approximate 2-fold since 1901, with peak net inputs in 1986 and 1976, respectively. Increases in NANI over time can primarily be attributed to high atmospheric N deposition, fertilizer N application and biological N fixation, while increases in NAPI are primarily due to increased fertilizer P application. Spatially, the hotspots for both NANI and NAPI have since the early 1950s shifted to the central sub-watersheds of the GRW, which can be attributed to greater urbanization and agricultural intensification in the central area. The historical NANI and NAPI estimates obtained for the GRW provide insights into the spatio-temporal patterns in NANI and NAPI, and can facilitate better N and P management strategies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".