An Effective Dose of Magnetic Field to Increase Sesame Plant Growth and Its Resistance to Fusarium oxysporum Wilt
Bibliographic record
Abstract
Globally, sesame consumption continues to increase, so it requires additional production to balance. Often there are obstacles in the production process, both technique and the presence of pests. One way to increase production is to treat it with a magnetic field during seed growth and germination. This study aims to determine the magnetic flux density (MFD) to obtain the optimal stem growth, chlorophyll content, fruit size, and plant resistance to Fusarium oxysporum. The seeds are processed by giving treatment using a MFD of 0.0-0.5 mT, which oscillates with a frequency of 50 Hz. Plant seeds were treated using a magnetic field every day for five days with a treatment time of 20 minutes each. The results showed that sesame seeds treated with magnetic fields had earlier emergence time of sprouts, higher stems, more chlorophyll content, faster flowering, larger fruit sizes, and more resistance to F. oxysporum wilt attacks. Treatment with a MFD of 0.3 mT resulted in optimal plant growth, health, and resistance to F. oxysporum attack. Treatment of seeds using magnetic fields ranging from wetting the seeds to germinating makes the plants healthier, bigger fruits, and more resistant to F. oxysporum wilt attacks.
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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) | 0.002 | 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".