Bibliographic record
Abstract
This article discusses engineering supervision and engineering efforts in remediation of different sites. Public Works and Government Services Canada, which manages many of the military bases in the Ottawa region, commissioned the environmental and geotechnical firm Golder Associates Ltd. to provide engineering supervision and support for remediation of the site. The remediation of the former military landfill site, conducted in February and March 2006, not only cleaned up a polluted part of the Earth, it also pointed to some of the current best practices being used to protect the environment when working on military properties. Once site remediation began, plans had to be changed quickly, because Golder discovered that parts of the landfill site were dotted with holes that indicated it was being used as a turtle nesting site. Through a literature review, bioscientists determined what kind of aggregate would be most acceptable to the turtles as nesting material. The landfill cap is vegetated with grass and shrubs to consolidate the soil cover layers and prevent their erosion. Given that the site is behind the target area for one of the firing ranges, it does not get much human traffic, but its landscape is in keeping with the surrounding area, and the site poses no more threat to the local ecosystem.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.155 | 0.065 |
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".