Evaluating the Impact of Climate Change Mitigation Strategies on the Optimal Design and Expansion of the Amherstview, Ontario, Water Network: Canadian Case Study
Bibliographic record
Abstract
The objective of this paper is to assess the impact of proposed Canadian climate change mitigation policies (discounting and carbon pricing) on cost, energy use, and greenhouse gas (GHG) emissions in the single-objective design/expansion optimization of the Amherstview water distribution system in Amherstview, Ontario, Canada. The single-objective optimization problem is solved with the elitist genetic algorithm (EGA). The optimization approach is used in a parametric analysis to examine the impact of discounting and carbon pricing on GHG reductions for cement-mortar ductile iron and polyvinyl chloride pipe materials. Preliminary results indicate that the discount rate and carbon prices investigated had no significant influence on energy use and GHG mass in the Amherstview system and did not meet the emission-reduction targets set by the Canadian government. This result was attributed to a number of factors, including adequately installed hydraulic capacity in the Amherstview system, the use of a time-declining GHG emission intensity factor, and the scope of the expansion problem.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".