Seasonality interacts with mixed land use and conservation in controlling patterns of nutrient and pathogen export from agricultural watersheds
Bibliographic record
Abstract
Fertilizer runoff is a significant source of nutrients to streams draining agricultural watersheds and results in numerous downstream impacts including eutrophication and coastal hypoxia. Additionally, pathogen export linked to manure application poses a threat to human health and results in recreational closures. Agricultural conservation practices, such as the planting of winter cover crops (CC), can reduce nutrient losses to streams, but their impacts on pathogen transport remain understudied. From 2019 to 2022, we sampled every 2 weeks in three subwatersheds of the Paw Paw River Basin (Michigan, USA) to assess the role of CC in reducing dissolved nitrate (NO 3 – -N), ammonium (NH 4 + -N), soluble reactive phosphorus (SRP), and the fecal bacterium Escherichia coli export. The three subwatersheds contain varying levels of agricultural land use, ranging from 41 to 77 %. Water column NO 3 – -N (mg L -1 ) peaked during the winter and spring fallow season, while E. coli (CFU 100 mL −1 ) peaked during summer, which points to different drivers controlling NO 3 – -N and pathogen export throughout the year. Increased daily yields of dissolved nutrient tended to coincide with more agricultural land cover; however, we measured highest daily yields of E. coli in the watershed with lowest agricultural land cover. Planting CC reduced NO 3 – -N yield by 10–31 %, NH 4 + -N yield by 19–22 %, SRP yields by 3–11 %, and E. coli yields by 17–48 %, and therefore is effective at mitigating both nutrient and pathogen export from agricultural landscapes, but additional work is required to fully understand the dynamics (timing and drivers) controlling E. coli export in watersheds of mixed land use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".