Seasonal and spatial patterns in phosphorus and nitrogen delivery: Implications for monitoring and management
Bibliographic record
Abstract
There is growing recognition that management efforts to limit harmful algal bloom (HAB) production in lakes need to consider tributary loadings of both phosphorus (P) and nitrogen (N). This may be the case for Lake of the Woods (LoW), which experiences annual HABs, and has been historically monitored for P, but not N. Ongoing agricultural intensification within the basin, including expansion of tile-drained row crop production, is creating new sources of N that may be entering rivers to a greater extent in the winter/spring, when sampling is typically less frequent. To address this gap, we investigated seasonal P and N inputs to the Rainy River, the largest tributary that feeds the LoW, from seven tributaries that drain the Lower Rainy River basin. Total P (TP) concentrations were consistently high at all seven tributaries and exceeded water quality guidelines, and total Kjeldahl-N (TKN) levels, which include ammonium (NH 4 -N) but largely reflect organic N, were also high relative to reference conditions for the region. In contrast, nitrate (NO 3 -N) levels were generally low, especially in the growing season. Notably, NO 3 -N, TKN, and TP concentrations were highest in tributaries with more agricultural development. There were no clear seasonal patterns in TP or TKN, whereas NO 3 -N was up to 10 times higher in the winter compared with the growing season. Higher N losses from agricultural areas that were especially clear in the winter suggest that N export is sensitive to regional trends of agricultural intensification and winter warming and warrant increased scrutiny of N inputs to the basin.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 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".