Decentralised water reuse in Sydney, Australia: drivers for implementation and energy consumption
Bibliographic record
Abstract
Water recycling and reuse is becoming increasingly common throughout the world. The objective of this study was to compare five decentralised water recycling and reuse systems in Sydney, Australia, in terms of the drivers for their implementation and their ongoing energy consumption, allowing comparison to conventional water sources. The security of supply was found to be the main driver for four out of the five schemes. For the fifth scheme, the driver was to obtain a high environmental rating for the building it is located in. The analysis shows that water reuse can provide water at the same or less energy consumption compared to water supplied through the mains network. However, where the water recycling ethos of ‘fit for purpose’ is not considered, this can often lead to a significant overall increase in power consumption. This study highlights the need for regulatory bodies to consider a wider range of impacts when preparing guidelines and incentive schemes for water reuse. When the focus is too narrow, there is a risk that unintentional negative impacts such as increased power consumption and potential carbon dioxide emissions are the outcomes.
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 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 | 0.001 |
| 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".