Promoting pepper (Capsicum annuum) photosynthesis via chloroplast ultrastructure and enzyme activities by optimising the ammonium to nitrate ratio
Bibliographic record
Abstract
Optimal plant growth in many species is achieved when the two major forms of N are supplied at a particular ratio. In this pot experiment, the effects of five different ammonium:nitrate ratios (ANRs) (0:100, 12.5:87.5, 25:75, 37.5:62.5, and 50:50) on photosynthesis efficiency in chilli pepper (Capsicum annuum L.) plants were evaluated. The results showed that an ANR of 25:75 increased the contents of chl a, leaf area and dry matter, whereas chl b content was not affected by the ANRs. Regarding chlorophyll fluorescence, an ANR of 25:75 also enhanced the actual photochemical efficiency, photochemical quenching and maximum photosynthetic rate. However, the 0:100 and 50:50 ANRs resulted in higher values for nonphotochemical quenching. An inhibition of maximal photochemical efficiency was found when 50% NH4+ was supplied at the later stage of plant growth. The addition of 25% or 37.5% NH4+ was beneficial for gas exchange parameters and the 25% NH4+ optimised the thylakoid of chloroplasts. Compared with nitrate alone, 12.5–50% NH4+ upregulated glutamate dehydrogenase (GDH), the large subunit and the small subunit of Rubisco. It can be concluded that the 25:75 ANR accelerated N assimilation through active GDH, which provides a material basis for chloroplast and Rubisco formation, resulting in the increased photosynthetic rate and enhanced growth in chilli pepper.
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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) | 0.000 | 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".