Foliar nano-fertilization enhances fruit growth, maturity, and biochemical responses of date palm
Bibliographic record
Abstract
The experiment was conducted in the Abi Al-Khaseeb orchard, Basrah, Iraq, during the 2019 season, on date palm (‘Hillawi’). The effect of foliar nano-fertilizer on the response of the growth and fruit ripening rate was positive. Adding nano-fertilizer to the annual date palm fertilization program improved growth and increased production. A comparison was done of foliar-applied NPK (traditional; 1 and 2 g·L −1 ), nano-fertilizer, and a combined treatment. The results revealed that the treatment of traditional foliar fertilizer and nano-fertilizer together increased the weight of fruit and bunches, water content, indoleacetic acid, and gibberellic acid relative to other treatments. Nano-fertilizers (1 g·L −1 ) led to an increase in fruit ripening rate, dry mass, total soluble solids, activity of the enzymes peroxidase and superoxide dismutase, and abscisic acid content. The leaflet protein expression shows that the appearance of protein bands 1 to 5 and 6 was upregulated by the control and traditional fertilizer, whereas the protein bands 6 and 7 were downregulated under nano-fertilizer. Hierarchical cluster analysis of proteins in the leaf in response to traditional fertilizer and nano-fertilizer showed two distinct clusters. The use of nano-fertilizer alone leads to the acceleration of fruit ripening, while the fruit production is increased using foliar nano-fertilizer with traditional fertilizer.
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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) | 0.001 | 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".