Implementing Weigh-in-Motion for Generation of Carbon Offset Credits in Canada
Bibliographic record
Abstract
In 2002, Canada ratified the Kyoto Protocol and committed to reducing its greenhouse gas (GHG) emissions by six percent from 1990 levels by 2012. Canada remains committed to working towards reducing GHG emissions and has developed an Offset System to encourage industry to develop methods of reducing GHGs. This Offset System requires specific procedures for quantification, data management and verification by a third party that must be followed and maintained to qualify for the Compliance Carbon Market for carbon credits. Weigh-in-motion (WIM) and other intelligent transportation systems (ITS) have been shown to improve efficiencies in trucking while still enforcing weight and dimension legislation to protect roadway infrastructure. With the implementation of these technologies, the amount of GHG emissions generated from trucking enforcement requirements may be reduced. This paper reviews how specific WIM and ITS technologies can be implemented to meet the carbon emission reduction quantification, data management, verification of data and reporting procedures that are required to be maintained and reported under Canada’s Offset System for Greenhouse Gases. The case study presented reviews two scenarios of implementing ramp and mainline WIM sorting systems integrated with various ITS technologies compared to the use of traditional static scales. The findings show that with the implementation of various WIM and ITS technologies there is a significant decrease in the delays trucks experience resulting in a reduction of GHGs produced and the generation of carbon credits that may be sold for revenue by an agency.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".