Soil nitrous oxide emissions most sensitive to fertilization history during a laboratory incubation
Bibliographic record
Abstract
A 12 wk laboratory incubation examined the effects of application of various nitrogen (N) and sulfur (S) fertilizers on soil plant-available nutrient levels and nitrous oxide (N2O) gas emissions with respect to soil fertilization history using soils sampled from the University of Alberta Breton Classical Plots. Fertilization history and added fertilizer treatments showed significant effects on N2O emissions and NO3−-N and SO4−-S recovered on ion-exchange resins over the 12 wk. Mean cumulative N2O emissions ranged from 0.43 to 1.18 kg N2O-N ha−1. The relationship between observed total resin-recovered NO3−-N and N2O emissions was not consistent for soils receiving long-term applications of various combinations of N, phosphorus, potassium, and S fertilizers. The N2O emission from two soils with a history of long-term N fertilizer applications but different S fertilization histories was significantly different even though resin-recovered NO3−-N levels were similar. When grouped according to added fertilizer treatments, mean cumulative N2O emissions showed a strong linear relationship with mean resin-adsorbed NO3−-N production. We hypothesize that the differences in the relationship between NO3−-N production and N2O-N emissions for soils with different long-term fertilization histories may be a result of the interaction of N and S oxidation processes. Further, soil fertilization history may significantly influence soil N2O emissions in response to N fertilizers added within the growing season of observation but isn’t often considered in short-term experiments, and this may be a significant source of uncertainty in the estimation of greenhouse gases inventories from agricultural soils.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".