Bibliographic record
Abstract
The Regional Municipality of Halton recognizes the inherent environmental issues associated with construction projects, such as pollution, disruption to the local communities, traffic interruptions, and high resource consumption. To help address these concerns, the Region adopted a Green Procurement Policy that integrates sustainability considerations into the decision process at all phases of a project's (and product's) lifecycle. For three recent road resurfacing projects, this forward-thinking approach allowed the Region to realize improved environmental net benefits, as well as cost-savings, through implementing more sustainable technologies, and getting them to work in the real world. Traditional road resurfacing projects involve pavement removal and disposal, production, transportation, and placement of asphalt. These activities consume considerable energy and resources, as well as disrupt the local community. Recognizing this, efforts were made during the planning stage to find greener methods. Detailed pavement condition assessments undertaken at the planning stage found that three innovative, green solutions could be used: CIREAM (cold-in-place recycling with expanded asphalt mix), SAMI (stress absorbing membrane interlayer), and pulverized asphalt. Although these technologies are not new, the Ontario engineering and construction industry has been relatively slow to adopt their regular use, mostly due to general unfamiliarity and uncertainty about their historical track records. 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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.013 |
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".