Evaluation of different nitrogen use efficiency indices using field-grown green bell peppers (<i>Capsicum annuum</i> L.)
Bibliographic record
Abstract
The effects of increasing nitrogen (N) fertilization on N use efficiency (NUE) and yield of green bell pepper were assessed in five field experiments over 2004 and 2005. These data were used to evaluate and contrast conclusions drawn from among 12 different NUE indices. In two diferent years (i.e., cool/wet vs. warm/dry), marketable yield response to N application was either positive or no response was observed. Total percent N in the fruit and shoot was lower in non-fertilized plants compared with plants grown in plots that received 70 or 210 kg N ha-1. There were considerable differences among locations in soil mineral N, yield, NUE, and plant N uptake and removal. For all eight fertilizer- and soil-based NUE indices assessed, NUE decreased as N application increased. However, for plant-based NUE indices, there was no difference in NUE values between N treatments. Thus, the interpretation and applicability of NUE depends on the goals of the research and the index used. Key words: Nutrient use efficiency, green bell peppers, harvest index, nitrogen, fertilizer, vegetable
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".