Bibliographic record
Abstract
Abstract. The Centre Block of Canada’s Parliament buildings, National Historic Site of Canada is set to undergo a major rehabilitation project that will take approximately 10 years to complete. In preparation for this work, Heritage Conservation Services (HCS) of Public Services and Procurement Canada has been completing heritage documentation of the entire site which includes laser scanning of all interior rooms and accessible confined spaces such as attics and other similar areas. Other documentation completed includes detailed photogrammetric documentation of rooms and areas of high heritage value. Some of these high heritage value spaces present certain challenges such as accessibility due to the height and the size of the spaces. Another challenge is the poor lighting conditions, requiring the use of flash or strobe lighting to either compliment or completely eliminate the available ambient lighting. All the spaces captured at this higher level of detail were also captured with laser scanning. This allowed the team to validate the information and conduct a quality review of the photogrammetric data. As a result of this exercise, the team realized that in most, if not all cases, the photogrammetric data was more detailed and at a higher quality then the terrestrial laser scanning data. The purpose and motivation of this paper is to present these findings, as well provide the advantages and disadvantages of the two methods and data sets.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 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".