Critical Nitrogen Curve and Nitrogen Nutrition Index for Corn in Eastern Canada
Bibliographic record
Abstract
Plant‐based diagnostic methods of N nutrition require the critical N concentration (Nc) to be defined, that is the minimum N concentration necessary to achieve maximum growth. A critical N curve (Nc = 34.0W−0.37 with W being shoot biomass in Mg DM ha−1), based on whole plant N concentration, was determined for corn (Zea mays L.) in France. Our objectives were to validate this critical N curve in eastern Canada and to assess its plausibility to estimate the level of N nutrition in corn. Shoot biomass and N concentration were determined weekly during the growing season at three sites for 2 yr (2004 and 2005); four to seven N treatments were used at each site. Data points were divided into two groups representing either nonlimiting or limiting N conditions according to significant differences in shoot biomass at each sampling date. All data points included in the limiting N group were under the critical N curve and most data points of the nonlimiting N group were on or above the critical N curve, hence confirming the validity of the critical N curve determined in France. The nitrogen nutrition index (NNI), calculated as the measured N concentration divided by the predicted Nc, ranged from 0.30 to 1.35. A significant relationship between relative grain yield (RY) and NNI (RY = −0.11 + 1.17 NNI if NNI < 0.93 and RY = 0.98 if NNI > 0.93; R2 = 0.89) was determined. The critical N curve from France is valid in eastern Canada and the NNI calculated from that curve is a reliable indicator of the level of N stress during the growing season of corn.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".