Bibliographic record
Abstract
With shifting paradigms in usual climatic events and increased occurrence of flood hazards, vulnerability assessment and adaptation of road infrastructure is essential. Road pavements are critical in sustaining socio-economic activities and their vulnerability to flood hazards could have serious cost consequences. Therefore, a conscientious decision to consider pavement materials, designs and alternatives that are resilient to recurring flood events is desired. Based on previous investigations into how pavements types, classes and configuration respond to extreme events, concrete pavements are reported as better flood-resilient systems in countries that have experienced intense flooding and inundation. Although Canada has experienced some of the worst flood incidences in history and owns a number of concrete pavement infrastructure, no study has been conducted to better understand its performance under extreme conditions. To provide insight on concrete pavement flood response, the use of the state of the art AASHTOWare Pavement ME Design (PMED) program is employed to model various flood scenarios on concrete pavement types and configurations common to two Canadian provinces, Ontario and Manitoba. The performance of the various pavement classes in terms of flood resilience, service life and cost feasibility is analyzed and results provide insight on the resilience and adaptive capacity of rigid pavements to flood hazards in Canada.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".