Management of Drainage Water in the Holland Marsh of Ontario for Environmental and Agronomic Benefits
Bibliographic record
Abstract
Abstract. The Holland Marsh of Ontario, Canada, is a low lying intensively cultivated organic soil region of 8500 hectares, draining to Lake Simcoe. The main crops grown are carrots, celery, onions and salad crops. Subsurface drainage is essential for crop production, given the naturally wet conditions of these organic soils. Water table control is also important to prevent oxidation of the organic soils and to meet crop-water requirements during the growing season. The Marsh is dyked and farmland drainage for the Marsh is achieved by a pumping station. The drainage water contains excessive nitrogen (N) and phosphorus (P), which is a source of algal blooms in Lake Simcoe. The Lake Simcoe Conservation Authority has established a P reduction strategy. Managing the drainage water from the Holland Marsh is critical to achieving the strategy. Based on field measurements, modelling, and long-term observations of pump discharges and P loads, we discuss how water table management at the field scale combined with an improved basin drainage pumping strategy can potentially reduce P loads entering Lake Simcoe and still meet agronomic objectives. Our results show that water table management at the field level, combined with a more optimized and timely pumping strategy at the basin scale can reduce P loads entering Lake Simcoe.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".