Predicting soil nitrogen availability to grain corn in Ontario, Canada
Bibliographic record
Abstract
Predicting the soil-available nitrogen (N) to grain corn over a growing season in humid temperate regions is the key for improving fertilizer N recommendations. The objective of this study was to evaluate a suite of soil-N tests to predict soil N availability to grain corn over two growing seasons at 13 individual sites with long-term history of synthetic N fertilization in Ontario, Canada (13 site-years). At each site, fertilizer N was applied at various rates (0–224 kg N·ha−1) to determine the crop response to N fertilizer, relative yield (RY), and the most economic rate of N (MERN). Across the entire dataset, water-extractable mineral N (WEMN) was the only soil test that strongly correlated to both RY (r = 0.74**) and MERN (r = −0.56*) indicating that in grain corn fields with long-term history of N fertilization, mineral forms of N in soil solution can be used for fertilizer N recommendations in southern and eastern Ontario. We also provide evidence that grouping soils based on clay content could further refine fertilizer-N recommendations for grain corn in Ontario. A multi-year validation of the WEMN test with more field sites and development of a fertilizer recommendation table for this soil test are recommended.
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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) | 0.000 | 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 | 0.001 | 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".