Vegetative Propagation of Bubil Seeds and Tuber Dormancy Reduction in Porang (Amorphophallus muelleri Blume) for Shorter Harvest Time
Bibliographic record
Abstract
The development of vegetative propagation techniques for porang (Amorphophallus muelleri Blume) is crucial for augmenting plant populations.However, a significant challenge encountered during this process pertains to the plant's six-month dormancy period during the dry season.Both the bulbil seeds and tubers enter this dormant state as a natural adaptation mechanism, thereby prolonging the harvest timeframe.If you can wake Bubils and bulbs when they are dormant, it will shorten the waiting time for harvest.The current study proposes a methodology to overcome this dormancy period, with the aim of reducing the time to harvest.Traditionally, there has been a lack of effective treatments to alleviate the dormancy of bulbils during the dry season, leading to an extended harvest period of approximately three years.The proposed method involves the application of growth regulators to bulbil seeds and tubers during the dry season, followed by planting to obtain small to medium-sized tubers.Results indicate that the application of growth regulators markedly accelerates the germination of bulbil seeds and growth of porang tubers.Notably, almost all treated bulbils (95%) demonstrated premature shoot emergence.Consequently, porang farmers are projected to achieve a significant reduction in time to harvest large tubers, from three years down to one year.This study provides novel insights and practical solutions for the porang farming industry, potentially revolutionizing current farming practices and significantly enhancing crop yield within a reduced timeframe.
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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".