The Role of Organic Fertilizer and Tree Pruning on the Growth and Nitrogen, Phosphate and Potassium Uptake of Red Ginger in Sengon Agroforestry System
Bibliographic record
Abstract
Red ginger is one of the C3 plants that has the potential as an herbal medicine.This causes red ginger to be sensitive to high temperatures and light intensity.One effort that can be done is to implement an agroforestry system.The study aims to examine the effect of fertilization and tree pruning on the growth and nutrient uptake of red ginger.The study used a randomized block design with a nesting pattern.The first factor, pruning trees, had two levels: without pruning (1730-12270 lux) and with pruning of sengon stands (2430-24900 lux).The second factor, nested within the first, was fertilization with four levels: chemical fertilizer, corn cob compost, Indigofera tinctoria compost, and goat manure compost.The highest plant height in the treatment without pruning was 107.83 cm.Indigofera tinctoria compost nested in pruning treatment showed the highest number of leaves and nitrogen absorption, which were 99 leaves and 0.125 g.plant -1 .Corn cob compost nested in pruning treatment produced the highest phosphate absorption, which was 0.360 g.plant -1 .Nitrogen absorption was positively correlated with phosphate and potassium absorption.Compost and pruning application can increase the growth and nutrient absorption of red ginger in agroforestry.
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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".