Research on Application of Multi-Channel Selector in Centrifugal Model Test of Anchoring Slope by Frame Beam
Bibliographic record
Abstract
An anchoring frame beam is a very common form of support for reinforced slopes, especially in alpine regions. Centrifugal tests have proved to be an intuitive and effective means of investigating the mechanism of action of frame beams. However, the data acquisition system of the geotechnical centrifuge in service has the problem of a small number of acquisition channels. A multi-channel selector based on the existing acquisition system was proposed, designed, processed, and manufactured, and it was debugged, tested, and applied in a no-load centrifugal test, static pressure model test, and centrifugal model test. The results show that the acquisition mode of the multi-channel selector connected with a maximum of 288 sensors has been changed from “one-to-one” to “one-to-many”. Its influence on various sensor signals is negligible. The multi-channel selector can work normally, which communicates and feeds back with the remote controller in the 1–120 g no-load centrifugal test. In the static load model test, 162 sensor signals were well collected through it. And only 51 channels were used to effectively obtain the signals of 187 sensors in a 70 g centrifugal model test of an anchoring slope with a frame beam. The multi-channel selector can be successfully applied in different use environments, saving time and reducing the cost of obtaining a single set of data.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".