Assessment of Two Calcium Silicate Sources on Cucumber Under Water Restriction
Bibliographic record
Abstract
Silicon (Si) is beneficial for plants that are under unfavorable conditions. In this study conducted under greenhouse conditions at Chapingo Autonomous University, Si fertilization was tested to examine whether it affected yields, biometrics, physiological parameters, and nutritional attributes of cucumber plants cultivated at different moisture levels in the substrate. Fifteen treatments were tried in a completely randomized design, including three moisture levels (50-60%, 75-85%, and 90-100% of container capacity (CC) and five Si doses (0, 50, 100 mg L-1 SiO2, 2 g L-1, and 3 g L-1 Wollastonite). Uniform irrigation was applied until the 18th day after transplantation, and controlled irrigation was applied according to the moisture levels until the end of the experiment. 3 g L-1 of wollastonite under 75-85% CC enhanced cucumber yield by 24.9% in comparison to untreated plants, while no Si dose affected fruit length, diameter, weight, and total soluble solids. At 90-100% of CC, 50 mg L-1 SiO2 increased the aerial biomass production (ABP) of the plants by 9.3%, and at 50-60% of CC, 100 mg L-1 SiO2 generated 11.8% more aerial biomass than untreated plants. As Si did not affect root production, mainly at 50-60% of CC, it improved chlorophyll a, b, and the total content of the leaves. Different parts of the plant saw significant increases in N, P, K, Ca, Mg, and Si concentrations because of Si’s interactions with moisture levels. When the substrate is low in moisture, Si is likely to improve cucumber yields, physiological, and nutritional characteristics of the plant.
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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".