Case Studies of Scour and Erosion at Water Crossings
Bibliographic record
Abstract
TransCanada Pipelines Ltd. (TransCanada) owns and operates over 38,000 km of pipeline facilities, geographically situated throughout Canada and portions of the USA. These facilities cross diverse terrain such as the Rocky Mountains in the west, the flat prairies of central Canada and the Canadian Shield in Ontario and Quebec. Within the complex geographic terrain are numerous hydrologic regions that contain over 2700 pipline crossings of creeks, rivers and lakes. Many of these crossings have been subject to some form of streambed degradation and coupled with extreme flooding events, have resulted in reduction of cover over the pipeline and/or exposed pipeline crossings. These hydraulic conditions can further threaten the integrity of an exposed pipeline crossing when subjected to vortex shedding, horizontal load or impact from floating debris. Historically 8 exposures have been identified and investigated in Alberta and 3 of them have been chosen as case studies for this presentation (the South Saskatchewan River, the Red Deer River and the Simonette River). Each case study will detail the following; historical events leading up to the incident, hydrotechnical analysis, the decision processes the design approach and implementation.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".