Media Growing Techniques and Different Soil Types to Increase Agronomic Characteristics and Content of Flavonoid Compounds on Dayak’s Onion (Eleutherine palmifolia Merr.)
Bibliographic record
Abstract
Utilization of biodiversity in the form of medicinal plants is an alternative to maintain health.Dayak onions as efficacious medicinal plants, have the potential to be developed.The optimal benefits of Dayak onions can be obtained if the raw materials used are of high quality.Environmental factors greatly affect the quality and quantity of tubers, including cultivation techniques and soil types.This research aims to study various cultivation techniques and soil types to improve the agronomical characteristics and flavonoid content of Dayak Onion.The study used a factorial randomized block design,3 repetitions.Factor I was the type of soil (T1=peat soil; T2=sandy soil).Factor II was the type of cultivation technique B0=without fertilization; B1=organic fertilization (chicken manure 20 t.ha -1 ); B2=inorganic fertilization (200 kg.ha -1 urea,150 kg.ha -1 SP-36,200 kg.ha -1 KCl); B3=combination of organic+inorganic fertilization.The results showed that the interaction treatment of various cultivation techniques and soil types had a significant effect on plant height, number of leaves, number of saplings.The combination of organic+inorganic fertilizers (chicken manure 20 t.ha -1 and 200 kg.ha - urea+150 kg.ha -1 SP-36+200 kg.ha -1 KCl) planted on peat soil types gives the best results on tuber wet weight 48.27 g/clump, dry tubers 16.73 g (12 wap) The average content of flavonoids planted on peat soil was higher 64.75 compared to sandy soil 52.33.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".