CANADIAN FORCES EXPERIENCE IN SLIP FORMING AIRFIELD PAVEMENTS
Bibliographic record
Abstract
The project at Canadian Forces Base Cold Lake in Alberta involved constructing a 10,000-foot runway in response to urgent pilot training needs. The base faced a rush job due to limited surveys and advanced planning. The runway's previous structure consisted of layers of asphalt and gravel over granular subbase. However, its asphaltic surface deteriorated quickly, prompting resurfacing attempts that didn't hold up. In the new project, engineers decided on a slip-formed overlay to replace the old surface. This method was crucial because of strict timelines and soil stability issues. The new pavement used grooves to improve drainage, and pilots reported better braking and a smoother landing experience. The project was completed ahead of schedule, with only minor cracking after a year, suggesting it would significantly outlast the old surface. Despite some issues with sealant bonding, the project was deemed a success. The quality of the new pavement, its stability, and excellent riding qualities offered an improvement that met the Canadian Forces' needs. (Abstract generated by AI tool ChatGPT 4)
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".