Then and now: Revisiting nutrient export in agricultural watersheds within southern Ontario’s lower Great Lakes basin
Bibliographic record
Abstract
An enhanced understanding of nonpoint source (NPS) nutrient export to the lower Great Lakes is needed to inform land use and land management decisions within southern Ontario. However, this understanding is limited by a lack of long-term, temporally-intensive monitoring. To address this knowledge gap, we revisit six agriculturally-dominated subwatersheds in southern Ontario, which were intensively studied during the mid-1970s, to assess changes in hydrology and NPS nutrient contributions. We compared 1975–1977 to 2016–2018 stream runoff, nutrient export (kg/day∙km2), and flow-weighted mean concentrations (FWMCs) of total phosphorus (TP), total dissolved phosphorus (TDP), total nitrogen (TN), nitrates (NO3–+NO2–) and Total Kjeldahl Nitrogen (TKN). Relative to the 1970s, runoff increased at three of six watersheds (by ~20–35%) while TP and TDP export increased at five watersheds (by ~50–125%). The increases in TP and TDP FWMCs were lower relative to phosphorus export changes at the three watersheds with increased runoff, suggesting that hydrology is an important driver of phosphorus export at these sites. Interestingly, export of TN and nitrates increased while TKN export decreased at most watersheds. We further note a shift in the timing of nutrient export at most sites, with ~40–70% of export now occurring during the winter and fall seasons whereas ~40–85% of past export occurred during spring and summer. These findings support an enhanced importance of non-growing season nutrient export from agricultural watersheds since the mid-1970s and stresses the need for targeted best management practices specific to the fall and winter seasons.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".