Bibliographic record
Abstract
The network of multi-lane motorways (trails) in Calgary, Canada, has struggled to cope with the city's growth and increasing number of vehicles. The Deerfoot Trail (Highway 2), running north-south through the city, is six-lanes for much of its length, but included a two-lane roadway at the south of the city. UMA Engineering Ltd was awarded the contract to improve the Deerfoot. The project would include an 11km extension, including three interchanges, a river crossing and provision for wildlife to cross the roadway. UMA was lead engineering firm for the entire project, supported by Amec Infrastructure Ltd, Associated Engineering (Alberta) Ltd and Amec Earth and Environmental Ltd. One innovation was a major fork for north bound vehicles exiting onto the Macleod Trail. This eliminated the need for a traditional right exit and reduced the need for one grade separated structure. Shifting the highway alignment avoided interfering with a side channel to the Bow River. Wildlife corridors were constructed along both banks of the river and under the bridge. The bridge construction used 65.3m girders, the longest used in North America. A pond was constructed to catch sediment-laden runoff from the bridge and roadway. The lighting system was designed to minimise light pollution and reduce glare.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.140 | 0.026 |
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".