Inuvik Super School VR Documentation: Mid-Project Status
Bibliographic record
Abstract
Over the last eight years the Construction Engineering and Management team at the University of New Brunswick have developed technologies to document the status of on-site progress. The evolving system, referred to as VR Doc, presents high-resolution, virtual reality panoramas of on-site operations in an interface that allows the user to explore the construction site throughout the project timeline. Since 2006 VR Doc has been used on six major projects, in particular on the Inuvik Super School for the Government of the Northwest Territories Department of Public Works and Services. This paper is a case study of VR Doc use. A variety of challenges have been overcome. These include temperature and lighting challenges during the photography step, processing challenges due to the low light level, and transfer challenges due to the file sizes. Continuing challenges include constraints on local personnel for on-site capture of the images as well as the integration of this new technology into traditional management processes. To date the greatest value from VR Doc has been as a communication medium for individuals within the Government of the Northwest Territories who are not involved in the project on a day-to-day basis but benefit from a fast visual record of the project. This case study is of interest to those who wish to understand cutting edge technologies for documenting construction progress. Possible roles of these technologies are: as a means of remotely monitoring project progress, as a pre-emptive means of resolving claims, as photographic as-builts for future reference, and as a training tool for personnel embarking on a similar project.
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.012 |
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".