Life Cycle Environmental Assessment Using Athena LCA Tool: A Manitoba Case Study
Bibliographic record
Abstract
Environmental sustainability is one of the four strategic priorities of the Department of Manitoba Infrastructure. Life Cycle Assessment (LCA) is acknowledged as one of the most comprehensive ways to evaluate the environmental impacts of different strategies associated with a physical feature. The Athena Pavement LCA software for highways is a tool that can be used to assess the environmental impacts of materials production, construction, and maintenance & rehabilitation activities over a given life cycle period. The software is also capable of modeling pavement vehicle interactions (PVI) to assess the environmental impact of traffic use phases of a roadway due to pavement surface roughness and deflection. This paper presents comparisons of the environmental impacts of various alternative strategies for a concrete pavement to demonstrate the opportunity to optimize pavement performance and environmental impacts. The concrete pavement constructed in 2015 on Manitoba Provincial Truck Highway 75 (PTH 75) has been used as a case study. A matrix of alternative concrete mix, pavement design, and maintenance and rehabilitation strategies has been used to compare environmental impacts of those alternative options. The analysis presented is expected to assist Manitoba Infrastructure and other agencies to better understand and weigh the environmental implications of alternative roadway materials, design as well as construction, maintenance and rehabilitation practices and select the best strategy considering pavement performance and preservation of our natural environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".