Depth distribution of mineralizable nitrogen pools in contrasting soils in a semi-arid climate
Bibliographic record
Abstract
A better understanding of the depth distribution of soil mineralizable nitrogen (N) pools is important to improve prediction of net soil N mineralization. However, our understanding of the depth distribution of these N pools under the semi-arid conditions of western Canada is limited. This study examined the depth distribution of soil mineralizable N pools (kS, the rate constant of a nondepleting zero-order stable N pool, and NL, the size of a depleting first-order labile N pool) of six sites in western Canada chosen to vary with respect to soil zone, soil texture, and cropping system. The depth distribution of mineralizable N pools varied substantially among sites, indicating that this distribution needs to be considered in making predictions of net soil N mineralization. A single regression equation including soil total nitrogen (STN), Pool I (a labile mineralizable N pool determined through a 14-day aerobic incubation), and soil pH explained 67% of the variation in kS across sites and soil depths. In addition, 95% of the variation in NL was explained by a regression model with Pool I. Thus, although the depth distribution of soil mineralizable N pools can vary substantially among sites, the mineralizable N parameters can be adequately predicted across sites and soil depths from simple soil properties. Comparison with a study using surface soils under humid conditions in New Brunswick suggests that the relationship between NL and Pool I is applicable across a wide range of soils, climatic zones, and cropping systems, whereas the regression model to predict kS varied with climatic zone, perhaps reflecting different pedogenic processes stabilizing the organic matter in these climatic zones.
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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 | 0.001 |
| 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".