A GIS Framework for Reducing GHG Emissions in Concrete Transportation
Bibliographic record
Abstract
Transportation is the largest Greenhouse Gas (GHG) emitting industry sector in North America. Between 1990 and 2005, emissions in Canada's freight transportation caused by Heavy Duty Diesel Vehicles (HDDV) increased by 18 mega tons of CO2 (Carbon Dioxide) or 84% equivalent. Although many factors contribute and while different options such as increasing fuel economy and substituting alternative fuel for reducing GHG emissions from transportation exist, little attention is given to reduce these emissions via behavioral changes. This research investigates lowering emissions in the construction industry through intelligent and optimized route planning. The task of concrete delivery from batch plants to construction sites was identified to estimate GHG emissions. An emission map in ArcGIS environment using the road network of the Greater Toronto Area (GTA) was developed and later implemented in a decision support tool that enables construction industry practitioners to estimate GHG emissions from concrete transportation before ordering concrete from batch plants. Since material cost, delivery time, concrete quality, and availability often are the main criteria for practitioners to select concrete batch plants, the developed work offers practitioners with a choice for the lowest possible GHG emissions during concrete transportation. Although a more sustainable concrete delivery becomes feasible using the developed approach, results demonstrate that ordering concrete from batch plant varies depending on the search criteria selected.
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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".