Bibliographic record
Abstract
To select the most appropriate pavement design for a given situation, it is necessary to understand how the pavement properties and in-service conditions relate to performance and life cycle cost. A given design may be most appropriate on one type of road and least appropriate on another type of road. This design selection is further complicated by the advent of new design methodologies, materials, and construction delivery techniques. Life cycle economic analysis is an important tool for comparing alternative treatment strategies. A life cycle analysis can use a deterministic approach, which incorporates a single point value, or it can use a probabilistic approach, which includes a mean, variance, and probability distribution. The probabilistic approach is better suited to describing the uncertainty associated with engineering. The Canadian Strategic Highway Research Program Canadian Long-Term Pavement Performance database and data provided by the Ministry of Transportation of Ontario were used in this analysis. Most construction variables are generally believed to be best described by a normal distribution. However, a lognormal probability distribution is better suited to describing these variables. This best fit is based on both a mathematical examination and a comparison of similar variables such as stocks and real estate values. It is also shown that thickness is a probabilistic variable that should be combined with the cost and incorporated into pavement life cycle costing. Ignoring the lognormal nature of these variables introduces bias into a life cycle cost analysis and does not reflect the true overall cost.
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 | 0.020 | 0.079 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.016 |
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".