Fertilization Methods and Varietal Selection Enhance True Shallot Seed Production in Lowland Indonesia: A Focus on Bauji Variety Performance
Bibliographic record
Abstract
Shallot cultivation in Indonesia nowadays still uses planting material in the form of bulbs that potentially carry viruses.The virus in the shallot bulb results in a decrease in productivity.Shallot botanical seeds, commonly called True Shallot Seed (TSS), are an alternative planting material to overcome this problem.Apart from being free from viruses, using TSS as plant material can reduce farming costs by 43% compared to bulbs.However, shallots' flowering and seed formation in the lowlands is still relatively low.Efforts have been made to use fertilizer application methods and select suitable varieties to support shallot flowering and seed formation.This research was conducted in a field with an altitude of 129 meters above sea level using a factorial complete randomized block design.The first factor is the fertilization method, consisting of broadcast and soluble fertilization.The second factor is shallot varieties, consisting of Bima Brebes, Bauji, Tajuk, and Batu Ijo.The results showed that the broadcast fertilization method on the Bauji variety gave higher seed weight per stalk (0.9 grams), seed weight per clump (1.19 grams), and seed weight per plot (4.30 grams) than other treatments.Fertilization of the soluble fertilizing method gives a faster flowering time than the broadcast method (5 weeks after planting).The Bima Brebes variety gives a faster flowering time than all varieties (4.3 weeks after planting), and the Tajuk variety gives a higher flower weight per clump (6.4 grams).
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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 | 0.001 | 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".