The Non‐Limiting and Least Limiting Water Ranges for Soil Nitrogen Mineralization
Bibliographic record
Abstract
A better understanding of factors controlling N mineralization would improve our ability to estimate fertilizer requirements more accurately. Net N mineralization approaches a small reaction rate at low and high water contents, giving rise to lower and upper limiting water contents and the least limiting water range (LLWR). Within the LLWR, there is a range in water contents in which mineralization is largely independent of water content, that is, the non‐limiting water range (NLWR). An incubation study was conducted to determine the LLWR and NLWR for five soils with different properties, and two relative compaction levels with and without the addition of a legume crop residue. These soils were incubated for 1 and 3 mo at eight water contents. Net N mineralization increased with incubation time and legume addition and varied curvilinearly with water‐filled pore space (WFPS). Logistic functions were generated to establish the relationships between net N mineralization and WFPS (%) and to calculate LLWR and NLWR. The mean NLWR was 32.2% after 1 mo and decreased to 18.1% after 3 mo whereas the mean LLWR was 55% after 1 mo and increased to 70.8% after 3 mo. The LLWR increased with organic C or total N but decreased with the addition of legume residue. The NLWR decreased with clay content and with the addition of legume after 3 mo. Emissions of N 2 O were greatest at water contents near the upper limit of the LLWR. Use of the NLWR to differentiate soils on the basis of the sensitivity of N mineralization to variation in water content is illustrated.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".