THE CALCULATION OF ULF ENVIRONMENTAL MAGNETIC FIELD GENERATED BY THE RUNNING TRAIN AND ITS APPLICATION IN DESIGNING A NEW RAILWAY IN SOUTHERN CHINA
Bibliographic record
Abstract
During recent years, people have attached great importance to the study on the effects of the environmental magnetic field generated by the railway and running train. In order to obtain the field strength at the distance of 1.1km and 2.1km away from the railway, we discussed the field from two points of view: theoretical calculation and actual measurement. Firstly, we measured and estimated the susceptibility of the train and its loading, then computed the magnetic field generated by them. After that, we measured the field in situ along the Han-Dan line by using the ENVI MAG proton magnetometer produced in Canada, which has a resolution of 0.1nT. The result indicates that, due to the diurnal variation of the geomagnetic field, the environmental magnetic field is hard to be measured directly at the distance of more than 300m away from the railway. So the theoretical calculation is of great significance, for it provides with useful information that the magnetic field caused by the running train is capable of meeting the requirement of not more than 0.1nT at the distance of 1.1km away from the railway, as well as its gradient less than 0.03nT/100m when 2.1km away from the railway. That is to say, the magnetic field has a variation of less than 0.03nT at two points which are 100m apart.
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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 | 0.001 | 0.001 |
| 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".