Effects of different foliar nitrogen fertilizers on cellular nitrogen metabolism and biomass of two shrub willow cultivars
Bibliographic record
Abstract
We examined the effects of foliar supplementation of different nitrogen sources (urea, Nitamin, NH4NO3, and arginine) to study their efficacy as fertilizers for growth of two clonally propagated shrub willow cultivars; namely, ‘Fish Creek’ (Salix purpurea L.) and ‘Preble’ (Salix viminalis L. × (Salix sachalinensis F. Schmidt × Salix miyabeana Seemen)). Our objectives were to determine (i) if foliar nitrogen application is an effective method of fertilization for the two shrub willows and (ii) if different nitrogen sources are metabolized similarly by the plants. The analyses involved soluble leaf polyamines, amino acids, total protein, total nitrogen and carbon, and plant biomass in response to short-term treatments with four sources of nitrogen. The effects of foliar nitrogen application on leaf chemistry, biomass, and foliar nitrogen content varied according to the form of nitrogen used. The data indicate that (i) urea is the most suitable nitrogen source for foliar spray (29% higher N accumulation vs. Nitamin), whereas arginine is the least suitable, and (ii) different nitrogen sources are metabolized differently by the plant. While the foliar nitrogen application method could become a practical and sustainable way to fertilize shrub willows and other short-rotation biofuel crops, it may also help reduce nitrogen loss to the environment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".