Deck Wearing Surfaces for the Yukon River Bridge
Bibliographic record
Abstract
The Yukon River Bridge in interior Alaska is recognized as an outstanding civil engineering design. The structure is a six-span 884.4-m steel bridge on a 6 percent grade. Two 155.9-cm wide by 414.0-cm deep steel box girders support an orthotropic steel deck. The structure carries the oil pipeline, vehicles, tourism busses and heavy trucks. In addition to highway traffic and the oil pipeline, there is potential for the structure to carry a future gas line. Weather can vary from harsh winter temperatures combined with snow and ice to mild summer temperatures. This structure was designed in the early 70's with a temporary 127-mm two-layer timber deck-wearing surface. Since then, timber running planks have been replaced three times with similar timbers (1981, 1992, and 1999). In 1992, in addition to running planks, timber deck planks were replaced and the steel deck was cleaned and coated against corrosion. A successful wearing surface for this bridge must be cost effective, lightweight, abrasion resistant and provide traction; especially during winter months. A wearing surface selection criterion is discussed and wearing surfaces on similar bridges are examined. Two examples illustrate a design procedure for selecting a wearing surface on this structure; design charts are used. The design charts account for truckloads and temperature change. No charts are available for traction or abrasion.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".