Influence of Perlite and Jiffy Substrates on Cucumber Fruit Productivity and Quality
Bibliographic record
Abstract
Most studies related to the culture of cucumbers refer only to germination, emergence and development of leaves and fruit, the nutrients used and their efficiency. This study assesses the influence of different types of soilless substrates (Perlite – 2 mm, Perlite – 4 mm, Perlite – 5 mm, Jiffy and Jiffy + 50% Perlite – 4 mm) on the content of nitrate, carbohydrates, chlorophyll, proteins, invertase activity and dry matter in cucumber leaves, stems and fruit grown on the respective substrates. The highest production was obtained on Perlite – 4 mm. The nitrates content was below the maximum admitted limit for all samples. Dry matter content of fruit depended on substrate in succession Jiffy, Perlite – 5 mm, Perlite 4 mm, Perlite 2 mm and Jiffy+Perlite 4 mm. The highest content of total chlorophyll was both in leaves as well as relating to fruit of plants grown on Perlite 5mm. The protein concentrations in fruits decrease in the order Jiffy, Perlite – 4 mm, Perlite – 5 mm, Perlite – 2 mm and Jiffy+Perlite – 4 mm. Both carbohydrates and invertase activity have the highest values in fruits. When, reducing sugars content is very high invertase activity corresponding is low. The highest level of reducing sugars was found in fruits grown on Jiffy followed by those developed on Perlite – 2 mm. Although, Jiffy substrate shows very good results it is expensive so the best choice for growing cucumbers in terms of both production and nutrient content is mainly the Perlite – 4 mm substrate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".