Nitrate leaching as affected by liquid swine manure and cover cropping in sandy soil of southwestern Ontario
Bibliographic record
Abstract
To assess the risk that liquid swine manure (LSM) application posed to groundwater quality and determine how to manage excess nitrates, LSM pre-plant injected at 75% (LSMlow) and >100% (LSMhigh) of corn (Zea mays L.) N requirements was compared to inorganic fertilizer (Fert), with (RC) or without (NC) a rye (Secale cereale L.) cover crop in 2 dry years (1999, 2001) and 1 wet year (2000) on sandy soil in Ontario. Corn yields in LSM and Fert treatments were comparable each year. When drainage potential was high, solution nitrates below the root zone in Fert (14 mg L-1) > LSM (7 mg L-1) in 1999, but in LSM (39 mg L-1) > Fert (13 mg L-1) in 2000. Occasionally in 2001, solution nitrates in LSMhigh > LSMlow and/or Fert plots, but drainage potential was low. Earlier N application in LSM (pre-plant) than Fert (77% of N sidedressed) plots in relation to rain events may have increased solution nitrates in LSM plots in 2000. Rye cover reduced solution nitrates from 8.8 mg L-1 (NC) to 4.3 mg L-1 (RC, average of all dates), regardless of nutrient source. In-season risk of NO3 leaching can be reduced by split application of N between pre-plant and sidedress, while overseeding cereal rye into standing corn minimizes leaching post-harvest (fall and spring). Key words: Zea mays, Secale cereale, pre-sidedress nitrate test, swine manure, nitrate leaching
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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 | 0.001 | 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".