The Use of a Time-Changing Magnetic Field to Increase Soybean (Glycine max) Growth and Productivity
Bibliographic record
Abstract
This study aimed to accelerate the growth and increase the productivity of soybean plants by providing treatment using a magnetic field at the time of seed germination. The research sample was soybean seeds of the Grobogan variety obtained from Center for Research on Various Tubers and Nuts. The magnetic field used in the treatment is in the direction of the magnetic flux density, which changes with time, and the change frequency is 100 times per second. The treatments were given with magnetic flux density from 0.0 mT to 0.5 mT for 20 minutes per day and repeated for five days. Treatment with a 0.1 mT magnetic field resulted in optimum values of germination emergence time, stem growth, chlorophyll content, early flowering time, weight per 10 seeds, and productivity. Treatment with a 0.5 mT magnetic flux density had a negative or no effect on the growth and productivity of soybean plants. Treatment using a magnetic field of 0.1-0.4 mT positively affected soybean growth and productivity, while 0.5 mT did not affect or negatively affect. The treatment of the magnetic field can have positive and negative effects depending on the magnetic flux density used.
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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".