Impact of leaching and denitrification on temporal distribution of nitrate in several Manitoba soils
Bibliographic record
Abstract
Leaching and denitrification are governed by several factors including soil management, moisture, aeration, organic carbon and soil temperature. The interaction of these factors and their effects on the processes of denitrification and leaching influence the temporal and spatial distribution of nitrate in soils. Nitrate leaching in the field was monitored on an Orthic Black loamy fine sand (Stockton soil), an Orthic Black loam (Wellwood soil) and a Gley d Rego Black Almasippi sand. Four treatments were examined: fertilizer applied (100 kg ha-1 N as NH4NO3) on fallow, fertilizer applied and seeded to wheat, no fertilizer applied on fallow and no fertilizer applied on wheat. Samples were taken in 15 cm increments to a depth of 120 cm and analyzed for moisture and nitrate content, and groundwater samples were analyzed for nitrate content. Laboratory studies of nitrate disappearance rate were conducted on the Stockton and Almasippi soils using soil slurries incubated on a shaker at one of five temperatures: 5, 11, 16.5, 20.5, and 26.5C. The amounts of NO3-N remaining in the slurries after set incubation periods were graphed as a function of time. Although nitrates moved mainly with soil water, soil nitrate contents of planted and fallow treatments were significantly different, while fertilizer application had no significant effects on nitrate distribution and profile content at the application rates used. Laboratory studies on denitrification rates indicate that for the Stockton profile, the biological activity decreased exponentially with depth and increased with increasing temperature according to the Arrhenius equation. The Almasippi profile exhibited two rates of biological activity: one for the 0-15 cm depth and another for the 15-120 cm depth, presumably due to the rapid decline in soil organic matter content below 15 cm in this profile.
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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 | 0.001 | 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".