Bibliographic record
Abstract
Hydro-demolition techniques are being used for the refurbishment contract on Montreal's highway 720 Ville Marie tunnel following the delivery of a new Aquajet HVD Evolution robot to main contractor GTS. Hydro-demolition removes weakened concrete but leaves undamaged material intact. Computerised control plays a key role in the performance quality and design of hydro-demolition equipment, allowing the machines to carry out repetitive tasks automatically. In the Ville Marie Tunnel, approximately 1km of concrete on both sides of the Eastern portals of the tunnel requires replacement and reinforcement. Electrical and telecommunications cables will also be replaced, and fibre optics and improved lighting will also be installed. Operating in live traffic conditions with very heavy traffic flows and working at heights of up to 15m, it was essential that the contractor protected both its workforce and passing traffic from falling debris. As a result the robot is installed behind a protected frame and positioned on a telescopic handler for ease of access for the extended height operations. The Aquajet removed damaged concrete at the speed of several hydraulic jackhammers and more than 25-fold faster than hand-held hammers. Hydro-demolition also eliminates the risk of vibration damage to personnel, complying with European work practices.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".