Determination of a Critical Nitrogen Dilution Curve for Spring Wheat
Bibliographic record
Abstract
Plant‐based diagnostic tools of N deficiency can be based on the concept of critical N dilution curves describing whole‐plant critical N concentration (Nc; g kg−1 of dry matter [DM]) as a function of shoot biomass (W; Mg DM ha−1). This has been tested for several crops, including winter wheat (Triticum aestivum L.) but has not been tested for spring wheat. Our objectives were to determine a critical N dilution curve specific to spring wheat, to compare this curve with existing critical N dilution curves for winter wheat, and to assess the plausibility of using it to estimate the level of N nutrition. The study was conducted at six site‐years (2004–2006) in Québec, Canada, with four to eight N fertilization rates (0–200 kg N ha−1). Shoot biomass and N concentration were determined on five to eight sampling dates during the growing season, and grain yield was measured at harvest. A critical N dilution curve (Nc = 38.5 W−0.57) was determined for spring wheat and was different from those reported for winter wheat. The N nutrition index (NNI = Nobserved/Nc) calculated from this spring wheat critical N dilution curve was significantly related (R2 = 0.70; P < 0.001) to relative grain yield. This critical N dilution curve and the resulting NNI adequately identified situations of limiting and nonlimiting N nutrition and could be used to establish the N nutrition status.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".