A novel method for automated trace discontinuity mapping at the Kemano hydroelectric tunnels in Western Canada
Bibliographic record
Abstract
Abstract Mapping of geological structures, as well the subsequent generation of as-built drawings of mine and infrastructure tunnels, is a crucial step in the evaluation of excavation performance during and after construction. Mapping is typically a tedious paper-based process, which is occasionally done with the support of rugged tablets. In either case, geotechnical data is collected manually and then transcribed to a compatible format based on the software that is being used for post-processing or visualization. These traditional data collection practices offer limited quality control, decreased accuracy, and minimal standardization across geotechnical personnel. This paper presents an extension of a geotechnical mapping application, 3-Dimensial Axis Mapping (3DAM), for trace mapping using the RockMass Mapper to the drill and blast and TBM tunnels at the Kemano hydroelectric facility near Kitimat, Canada. The aim of this study is to use the Mapper to capture trace discontinuities in TBM and blasted tunnels, and to integrate the data into industry-standard CAD software that is already used by construction teams to generate drawings. This new application of the 3DAM method will assist in obtaining more accurate geotechnical data in a digital form, allowing for quicker data analysis and more reliable excavation design in the long term.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".