Bibliographic record
Abstract
The industry is a leading consumer of fuel and producer of greenhouse gas (GHG) emissions, inspiring federal and provincial governments to enact legislation and promote new technologies. This paper evaluates and compares five provincial green trucking programs; from Alberta, British Columbia, Manitoba, Nova Scotia and Ontario. These programs were funded by the provincial governments and administered by various industry groups and non-profit agencies. Green programs emanated from the Canadian Trucking Alliance's enviroTruck program, focusing on GHG emissions, fuel consumption and working conditions in heavy-duty across Canada. Each program was launched independently and had unique scope and mandate. This comparison looks at the following factors: stakeholders (e.g. funders, administrators and beneficiaries); number of tractors and trailers submitted for consideration by the industry; number of approved tractors and trailers by the programs; investment in technologies; and fuel conservation and emission reduction estimates. The paper presents key results from each program. It also offers public policy recommendations to facilitate improved practices, in view of current anti-idling and clean air legislation. Finally, there are recommendations for firms and private fleets regarding sustainable transportation best practices. (A) For the covering abstract of this conference see ITRD record number 201310RT334E.
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) | 0.000 | 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".