Carbon curves for the assessment of embodied carbon in the wastewater industry
Bibliographic record
Abstract
Abstract The water and wastewater industry has been tasked with reducing its greenhouse gas (or carbon) emissions. A key component of any emissions reduction strategy is emissions measurement. While operational emissions are reported by the sector on an annual basis, there is a lack of robust data on embodied carbon. The aim of this paper was to develop a practical solution for assessing the embodied carbon in wastewater assets. The analysis revealed a linear relationship between carbon emissions and capital investment in the construction of wastewater treatment works (1.3 tCO 2 /£1000) and wastewater pumping stations (0.3 tCO 2 /£1000). Carbon emissions from sewer construction were found to increase linearly with increasing pipe diameter, with ductile iron pipelines responsible for higher emissions than polyethylene. Operational carbon is the major component in the whole life carbon of wastewater treatment works, but future decarbonisation of the electricity grid may increase the relative importance of embodied carbon.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".