Effect of Unigel Plantum on Lettuce Quality
Bibliographic record
Abstract
Greenhouse farming is an innovative model of agriculture that promotes sustainable production. Advanced greenhouse complexes use hydroponics, which makes it possible to grow vegetables and salads as far north as in Russia’s polar regions and on Sakhalin Island. To improve the yield and quality of lettuce, local farmers need an efficient mineral wool substrate and hydroponics. Biodegradable gels in combination with soil microorganisms are known to stabilize and prolong the effect of organic and mineral complexes. The research featured Aficion green lettuce grown hydroponically in a greenhouse. The control plants were grown in line with the industrial technology, which involved a four-fold weekly foliar treatment with a combination of Ecogel and Agrocen at 0.5 and 0.15%, respectively. The experimental samples were grown on substrate treated with Yunigel Plantum at 0.03 g per pot. The weight of lettuce leaves without roots was determined after cutting; their moisture content was determined after drying to a constant weight. The quality of leaf lettuce was assessed by the content of solids (State Standard GOST 31640-2012), crude protein (GOST 13496.4-2019), and amino acids (M 04-87-2009). The effect of Yunigel Plantum on quality and yield was evaluated by the ripening period, weight, root development, root hair development, moisture content, protein, and amino acids. The study also involved the effect of three different concentrations of humic and fulvic acids (Beres-8) to identify the optimal concentration. Yunigel Plantum_12 increased the yield and growth rate by 20%: as it boosted the root development, the experimental lettuce absorbed nutrients and became rich in essential amino acids. Yunigel Plantum can be recommended for greenhouse lettuce farming since it proved able to increase the yield and improve the nutr itional value of lettuce.
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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) | 0.001 | 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".