Role of Foliar Application of Nicotinic Acid and Tryptophan on Onion Plants Response to Salinity Stress
Bibliographic record
Abstract
This experiment was conducted at the National Research Centre, Dokki, Cairo, Egypt during 2006 and 2007 winter season to evaluate the effects of spraying with tryptophan (TP) and nicotinic acids (NA) on onion plants grown under varying degree of salinity stress (irrigation water with 3000 and 6000 ppm salinity) as compared to tap water (300 ppm) irrigation. Irrigation by high salinity water decreased the number of leaves, mean bulb diameter, fresh and dry weight of bulb and whole plant. Foliar application of TP and NA significantly increased the top height and fresh weight, and bulb dry weight. The above beneficial effects were greater in the plants sprayed with NA as compared to those of the plants received TP spray. At the lower salinity level (3000 ppm), TP was more effective than NA in mitigating the negative effects of salinity stress. At the high salinity stress (6000 ppm), however, the converse was evident. The electrolyte leakage (EL) was not influenced by 3000 ppm salinity irrigation water as compared to that of the plants which received tap water (300 ppm) irrigations. However, it increased significantly in the plants irrigated by 6000 ppm salinity water. Lipid peroxidation (LP) and EL were significantly lower in the plants sprayed with TP or tapwater. In addition, spraying of NA or TP increased the plant growth and biomass weight. Therefore, the above foliar sprays appear to mitigate the negative effects of increased salinity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".