Sediment and Nutrient Contributions from Subsurface Drains and Point Sources to an Agricultural Watershed
Bibliographic record
Abstract
Excess sediment and nutrients in surface waters can threaten aquatic life. To determine the relative importance of subsurface drainage as a pathway for movement of sediment and nutrients to surface waters, loading from various tile systems was compared to that from sewage treatment plants (STP) within the same watershed. Movement through tiles comprised 1 to 8% of estimated total (overland plus tile) annual sediment loading from the respective areas drained by the tile. Load during the growing season from five closed drainage systems without surface inlets averaged 5 kg sediment/ha, 0.005 kg dissolved reactive P (DRP)/ha, 0.003 kg NH 4 -N/ha, and 3.8 kg NO 3 -N/ha; and from two open drainage systems with surface inlets averaged 14 kg sediment/ha, 0.03 kg DRP/ha, 0.04 kg NH 4 -N/ha, and 3.1 kg NO 3 -N/ha. The eight STP contributed about 44 530 kg suspended sediments, 3380 kg total P, 1340 kg NH 4 -N, and 116 900 kg NO 3 -N to the watershed annually. Drainage systems added less NH 4 -N and P, but more NO 3 -N and suspended solids to surface waters than STP. Tile drainage pathways for NO 3 -N, STP in the case of P, and overland pathways for sediment are indicated as targets to control loading in artificially drained agricultural watersheds.
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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 | 0.001 | 0.001 |
| 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".