Bibliographic record
Abstract
Of the geoelectromagnetic techniques, multi-electrode Direct Current (DC) and Transient Electromagnetic (TEM) methods are the most powerful tools for shallow soundings. For city active fault detection, urban noise is a key problem in the use of electrical or electromagnetic methods. Effects of some major noises on DC and TEM are discussed on the basis of the experiments carried out in Fuzhou City in 2001. The experiments show that underground noises (pipes, cables, etc.) are most harmful to DC soundings, while for TEM, in addition to the underground noises, the aerial noises (power lines, metal sheds, etc.) will also lead to serious effects. Even so, effective soundings can be obtained providing that the noises are not too strong and some proper countermeasures are taken. In the experiments, specific measurement environments, including aerial and underground power lines and cables, water supply pipelines, roads, metal sheds, waste disposal sites, etc., were chosen as the urban noise sources. A set of RESECS instruments from DMT, Germany, were used for DC test, and EM-47/EM-67 by Geonics, Canada, for TEM test. The results of the experiments show that for DC soundings if the underground noise is not too strong, an effective record generally can be obtained, and especially the results would be much improved if the sampling time window of the instrument could be adjusted according to the noise distribution. We strongly recommend, therefore, that an instrument with real-time display of injection current and measurement potential, having adjustable time window be used for the active fault detection in urban areas. The configuration of electrodes is also important in some cases. For TEM soundings, it is better to set the measurement traverse at least 50m away from power lines, roads, cables, and big pipes. If the pipes are not big and not densely distributed within the transmitter loop, good data can be obtained at the sites several meters away from the pipes. In an area with electric current channeling, different configurations of the transmitter loops of different dimensions should be tested before the measurement.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".