The significance of microwaves in the environment and its effect on plants
Bibliographic record
Abstract
Wireless technologies are becoming popular due to convenient lower implementation costs and operational costs compared with wired technologies. Various wireless internet and communication technologies, such as worldwide interoperability for microwave access (WiMAX) and long-term evolution (LTE), are expanding rapidly. As with mobile phones, all of these technologies operate using high-frequency electromagnetic waves in the microwave category (3 × 102 – 3 × 106 MHz). An increasing number of operators within a geographical area is resulting in high microwave densities in the environment. At the same time, wireless technologies are now utilizing radio frequency electromagnetic radiation of up to 5500 MHz, and frequency spectrum allocation tables indicate that countries have allocated additional high frequencies for broadcasting purposes. Scientists have widely investigated the effects of microwaves on humans and animals, and some findings confirm that such effects exist. In comparison, a very limited number of published studies have addressed the effects of microwaves on plants. The findings of these studies indicate that the effects of microwaves on plants depend on the plant family and growth stage involved as well as the exposure duration, frequency, and power density, among other factors. However, the number of published studies is not yet sufficient to support drawing strong conclusions regarding the effects of microwaves on whole plant communities. Therefore, further studies are necessary to support present findings and uncover new findings.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".