Enhancement of the Initial Growth Rate of Agricultural Plants by Using Static Magnetic Fields
Bibliographic record
Abstract
Electronic devices and high-voltage wires induce magnetic fields. A magnetic field of 1,300-2,500 Gauss (0.2 Tesla) was applied to Petri dishes containing seeds of Garden Balsam (Impatiens balsamina), Mizuna (Brassica rapa var. japonica), Komatsuna (Brassica rapa var. perviridis), and Mescluns (Lepidium sativum). We applied magnets under the culture dish. During the 4 days of application, we observed that the stem and root length increased. The group subjected to magnetic field treatment (n = 10) showed a 1.4 times faster rate of growth compared with the control group (n = 11) in a total of 8 days (p <0.0005). This rate is 20% higher than that reported in previous studies. The tubulin complex lines did not have connecting points, but connecting points occur upon the application of magnets. This shows complete difference from the control, which means abnormal arrangements. However, the exact cause remains unclear. These results of growth enhancement of applying magnets suggest that it is possible to enhance the growth rate, increase productivity, or control the speed of germination of plants by applying static magnetic fields. Also, magnetic fields can cause physiological changes in plant cells and can induce growth. Therefore, stimulation with a magnetic field can have possible effects that are similar to those of chemical fertilizers, which means that the use of fertilizers can be avoided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".