Plant‐Based Diagnostic Tools for Evaluating Wheat Nitrogen Status
Bibliographic record
Abstract
The nitrogen nutrition index (NNI), based on critical plant N dilution curves, was developed to determine the in‐season N status of many species including wheat (Triticum aestivum L.). We assessed the relationship between wheat NNI and two simpler diagnostic tools; namely, leaf nitrogen (NL) concentration and chlorophyll meter (CM) readings. The study was conducted at six site‐years (2004−2006) in Québec, Canada, using four to eight N fertilizer rates (0−200 kg N ha−1). Leaf N concentrations and CM readings were determined from the uppermost collared leaf during the growing season along with NNI determinations. Generally, NNI, NL concentrations, and CM readings increased with increasing N rates. Leaf N concentrations and CM readings were significantly related to NNI during the growing season. Normalization of the CM values, relative to high N plots (relative chlorophyll meter [RCM] readings), improved the relationship with NNI by reducing site‐year differences. However, variation among sampling dates was observed in all relationships. By restricting the sampling dates to essentially the elongation stage, the relationship between NNI and NL (NNI = −0.43 + 0.035 NL; R2 = 0.52), CM (NNI = −0.64 + 0.039 CM; R2 = 0.68), or RCM (NNI = −1.31 + 2.45 RCM; R2 = 0.82) was generally improved. Nitrogen concentration, CM reading, or RCM reading of the uppermost collared leaf, preferably at the elongation stage, can therefore be used to assess the nutritional status of spring wheat.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".