Effect of fertilizers on yield, phytochemical, and antioxidant properties of <i>Cucurbita moschata</i> fruits
Bibliographic record
Abstract
Abstract The nutritional and functional properties of squashes are influenced by various factors, such as the stage of plant development, soil composition, and type of fertilizer. This study evaluates the impact of various organic fertilizers on Cucurbita moschata D. properties. For this purpose, squashes were fertilized using Ash at 10 kg/25 m 2 , bovine compost at 62.5 kg/25 m 2 , and a 1:1 mixture of ash and bovine compost. Negative control (without fertilizers) and positive control (NPK 20‐10‐10 at 2 kg/25 m 2 ) were included. Post‐harvest, the yield, carotenoids, phenolic compounds, flavonoids, and antioxidant activities were assessed. Ash fertilizer resulted in the highest number of fruits per plant (2.20 ± 0.16). Regarding flavonoids, the bovine compost yielded the highest level (428.67 ± 2.62 mg/100 g of edible portion). The mixture of ash and bovine compost produced fruits with the highest content of total carotenoids, β‐carotene, lycopene, and total phenolic compounds (249.7 ± 3.68, 219.80 ± 3.41, 26.07 ± 0.41, and 575.00 ± 9.95 mg/100 g of edible portion, respectively). Moreover, carotenoids extracted from this mixture exhibited the highest inhibition of DPPH and ABTS free radicals (30.87 ± 0.65 and 47.32 ± 1.30%, respectively). This research suggests that the use of a mixture of ash and bovine compost could significantly enhance the yield of squashes and their phytochemical and antioxidant potential.
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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".