Investigating the Technical Feasibility of Utilizing Aquifer Storage and Recovery to Supplement the Public Water Supplies in Evans Head and Ballina
Bibliographic record
Abstract
At a time when the future of fresh water resources in Australia becomes more unpredictable as a result of global climate change, it will become necessary to look for new alternative sources of fresh water. Reclaimed wastewater is an important fresh water resource that will become increasingly important. One strategy to augment the public water supply is to inject and store reclaimed water underground, then to pump it out of the aquifer and treated to drinking water standards. This is known as Aquifer Storage and Recovery (ASR) and similar schemes have been established in the United States, United Kingdom, Canada, Australia, South Africa and Israel. This report examines the feasibility of using ASR to supply water to the towns of Evans Head and Ballina. Using available hydrogeological data, I analyzed the potential for each aquifer to transport the flow of wastewater. I also determined adverse interactions that may take place between the injected reclaimed wastewater and ambient groundwater, and how to treat these problems. Basic plans for treatment are advised. Based on the data I have analyzed, I have determined that pending further study, ASR is feasible in this region. While this report is by no means comprehensive, it provides a starting point for designing an ASR scheme in this area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".