Quantifying Greenhouse Gas Generation for Roadway Maintenance, Rehabilitation and Reconstruction Treatments
Bibliographic record
Abstract
Greenhouse gas (GHG) emission levels in Canada peaked in 2007 at 751 Mt CO2e (carbon dioxide equivalents) and currently these levels are decreasing. Through the Copenhagen Accord, Canada has committed to a 17 percent reduction of 2005 GHG emission levels by 2020 to 607 Mt. To reduce GHG emissions generated in roadway construction, it is important to quantify the amount of GHGs produced for various treatments and to identify which aspects of construction contribute the greatest. This paper describes the development of a probabilistic model that quantifies the amount of GHGs generated through maintenance, rehabilitation, and reconstruction treatments for flexible pavement structures and includes the GHG emissions generated from the transportation, production and placement of materials. The maintenance treatments reviewed include: fog seal, slurry seal, micro surfacing, chip seal and ultra thin overlay. The rehabilitation and reconstruction treatments reviewed include: cold in-place recycling, mill and fill, full depth reclamation, and use of offsite recycled and virgin materials for reconstruction. To quantify the GHGs generated for each of these treatments a case study of a typical lane-km (3,700 m2) is used. A case study quantifying the amount of GHG emissions generated through 33,888 m2 of roadway reconstruction in the neighbourhood of King Edward Park is presented. Through the use of full depth reclamation for reconstruction it is estimated that approximately 52 percent or 700 t CO2e less was generated compared to a traditional remove and replace with virgin materials. For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".