New High Speed, High Accuracy Underground Scanning Equipment
Bibliographic record
Abstract
Increasingly, underground activities, especially in mining, require monitoring of the movement or convergence of tunnel sections in order to model and characterise these movements and thus take preventive measures to eliminate or minimise the risk of collapse.Laser scanning is one of the most efficient ways of doing this.For this reason, it has been used in recent years for modelling and monitoring underground spaces [1], [2].Its working method is based on the collection of geospatial information through repeated measurements over a period of time.This makes it possible to define very precisely the horizontal and vertical displacements of the monitored space [3].Currently, in order to achieve the highest possible accuracy with laser scanners, measurements are carried out by an operator on foot, parking the laser scanner on a tripod approximately every 10 metres in a given section of the gallery to be scanned, until the entire section of the gallery to be measured has been completed.The problem with this system is that it exposes the operator to the risks present in the gallery, such as dust, gases, possible being run over or spontaneously falling stones [4].In order to avoid or reduce as much as possible the operator's exposure to these risks, a laser scanner measurement method is proposed, based on the "stop & go" method applied to an all-terrain vehicle, where the laser scanner is attached to the roof or another part of the vehicle using a suitable coupling.This coupling system must have certain characteristics in order for the scanner measurement to be safe and accurate [5].From here, the driver has a device to control the laser scanner via a wifi connection generated by the scanner itself.The vehicle then drives away from the first section of a gallery to be monitored, where on both sides of the gallery, or at least on one of the two sides, there is a topographical point or target materialised by a nail and reflective paint around it.The operator starts the laser scanner and takes a measurement of this first section of the gallery.Once this first measurement has been completed, he drives the vehicle forward for about 10 metres, stopping at the level of another target on the right and/or left wall, where he starts the laser scanner again to measure the second section of the gallery.This is repeated until the entire length of the gallery has been surveyed.The tests carried out on sections of mine gallery using laser scanners and the method described above have shown that it is a good method, since it has been possible to avoid exposing the operator of the scanner to the risks of an underground gallery, with the same precision as if the measurements had been carried out by the operator on foot.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".