Variability of the Illinois Soil Nitrogen Test across Time and Sampling Depth
Bibliographic record
Abstract
There is potential for using the Illinois soil nitrogen test (ISNT) to improve N fertilizer recommendations for crops in the southeastern United States. The ISNT has been previously calibrated to predict N rates for corn ( Zea mays L.) in North Carolina. This study evaluated the effects of sampling time, sampling depth, crop rotation, and fertilizer application on soil ISNT‐N during a 2‐yr period in the humid Coastal Plain and Piedmont regions of North Carolina. Ten sites were repeatedly sampled at 0‐ to 10‐, 10‐ to 20‐, and 20‐ to 30‐cm depths in fall, mid‐winter, and spring between October 2006 and May 2007. Illinois soil nitrogen test N, KCl‐extractable soil NO 3 –N and NH 4 –N, and soil organic matter (SOM) derived by loss‐on‐ignition (LOI) were evaluated at each sampling. Temporal changes in these soil parameters were evaluated for various crop rotations and N fertilizer applications. Soil ISNT‐N decreased with depth and showed significant variation with time at all three depths at all sites. Soil ISNT‐N was influenced by crop rotation and tillage but was not significantly affected by N fertilizer applications. Considering all sites together, ISNT‐N was well correlated with LOI; however, ISNT‐N was not correlated with LOI across time within sites. This suggests that the ISNT measured a fraction of SOM that behaved somewhat independently with time.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".