Timing of Nitrate Leaching from Turfgrass after Multiple Fertilizer Applications
Bibliographic record
Abstract
Abstract The leaching of nitrogen from surface-applied fertilizer to groundwater is an environmental concern. Nitrogen fertilizer is routinely applied to turfgrass from spring to late autumn in Canada. The main objective of this study was to determine the contribution of N applied in May, July and September to leaching. The leaching of applied chloride (May and September only) was also monitored and the transport of nitrate and chloride were simulated using the model LEACHM (within EXPRES) to assist in fulfilling the main objective. The accuracy of the model simulation for transport, not nitrogen losses, was also addressed. Field lysimeters (Guelph, Ontario) were packed with a three-horizon profile of a sandy loam soil, topped with Kentucky bluegrass (Poa pratensis) sod and monitored for 1 year. Based on soil water samples taken from suction samplers placed at depths of 10, 17, 29, 43, 54, 64 and 85 cm, part of the solute from spring/summer applications remained in the soil during the unusually dry summer. This residual solute was later transported downward with the ensuing infiltration front in late autumn, building upon the autumn application, resulting in excessive concentrations. Predictions by LEACHM of solute concentration profiles generally were similar to field measurements.
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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 | 0.000 |
| 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) | 0.000 | 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".