Post-shearing data collection with enhanced network smart markers
Bibliographic record
Abstract
Enhanced network smart markers (ENSMs) were installed at Teck’s Highland Valley Copper (THVC) mine in an actively moving slope where standard downhole instrumentation is often damaged within weeks of installation and rendered inoperable. The ENSMs were used to monitor downhole deformation in the active shear zone and provide movement and water pressure data after the standard inclinometer and vibrating wire piezometer (VWP) installations were sheared. ENSMs provide a new approach to monitoring subsurface deformation. As with any new technology, ENSM performance needed to be evaluated and validated for the data to be deemed reliable. The trial conducted at THVC was set up such that the ENSMs were installed along with traditional technology (inclinometers and VWPs). This enabled a direct comparison between the ENSMs and inclinometer/VWPs for the trial at THVC. ENSMs were installed as downhole arrays, attached to inclinometer pipe and standard piezometers in two boreholes located on the Valley pit southeast wall. As the Valley pit mining sequence progressed downslope of the boreholes, deformations and pore pressure readings were recorded. This paper presents comparative deformations taken over a period of several months recorded by the ENSM system, versus readings recorded using a traditional inclinometer system. Additionally, pore pressure readings recorded by the ENSM system are compared to data recorded by traditional grouted in-place vibrating wire piezometers. Deformations and pore pressure measurements generally correlate well, and discrepancies between the systems are discussed. There were many learnings from the trial, but the main success was following significant amounts of shearing in the wall, the ENSM system continued to yield data below the shear zone after the traditional instruments had been severed. This allowed for deformation and pressure data to be monitored below the shear for a longer period than was possible using traditional downhole slope monitoring methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".