Implementation of GHG Tracking Software for Sustainable Transportation Infrastructure Projects
Bibliographic record
Abstract
The benefits of sustainable business practices are well documented. The Canadian Precast/Prestressed Concrete Institute has provided the tools for its member plants that will have a measurable impact on their environmental and economic performance, using a customised industry software, the Sustainable Precast Concrete Benchmark Calculator (v1.0). The ultimate benefit is to the facility owner who can use the information to identify environmental hotspots and make informed decisions about the environmental impact to their transportation infrastructure project. The software, developed for CPCI by the Athena Sustainable Materials Institute (ASMI), enables manufacturers to measure their cradle-to-gate life cycle environmental footprint. Once a manufacturing facility enters their raw material usage, electricity, natural gas, gas, diesel, heavy fuel oil and liquefied propane gas usage the software uses ASMI's life cycle inventory database to calculate a set of sustainability indicators - global warming potential (GWP), total primary energy (PE) and water usage for the plant. The facility, as part of the overall CPCI Sustainable Plant Program, also self-evaluates and reports their environmental performance indicators - dust, noise and waste materials. Participating plants report their tracked results to CPCI on a quarterly basis, the results of which are presented in an annual industry report. Individual plants are also provided a customised report on a quarterly basis for their own internal benchmarking. Specifiers and owners can request the sustainability impacts on a project basis and are also encouraged to include this informational requirement in their contract specifications. (A) 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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.075 | 0.027 |
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".