The role of agricultural drainage, storm-events, and natural filtration on the biogeochemical cycling capacity of aquatic and sediment environments in Lake Erie's drainage basin
Bibliographic record
Abstract
Lake Erie is the most at risk of the Great Lakes for degraded water quality due to non-point source pollution caused by agricultural activities in the lake's watershed. The extent and temporal patterns of nutrient loading from these agricultural activities is influenced by the timing of agronomic events, precipitation events, and water flow through areas of natural filtration within the watershed. Downstream impacts of these nutrient loading events may be moderated by the co-loading of functionally relevant biogeochemical cycling microbial communities from agricultural soils. This study quantified loading patterns of these communities from tile drain sources, assessed whether functional communities from agricultural sources influenced downstream microbial functionality, and investigated how distance from agricultural sources, storm events, and areas of natural filtration altered nutrient cycling and nutrient fluxes in aquatic and sediment environments. Water and sediment samples were collected in the Wigle Creek watershed in Ontario, from tile drains through to Lake Erie, from May to November 2021, and microbial nitrogen (N) and phosphorous (P) cycling capacity (quantitative PCR), and nutrient levels were evaluated. Results showed that N and P functional groups were co-loaded with nutrients, with increased loading occurring during storm events and during agricultural activities including fertilization and harvest. Overall functional capacity in the aquatic environment decreased with distance from the agricultural sources and as water transited through natural filtration areas. In contrast, the sediment environment was more resilient to both agricultural disturbances and abiotic factors. This study expands our understanding of when and where different stages of N and P cycling occurs in agriculturally impacted watersheds, and identifies both seasons and regions to target with nutrient mitigation 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".