Wastewater Automation – The Development of a Low Cost, Distributed Automation System
Bibliographic record
Abstract
In developed countries wastewater management is considered a vital aspect of community health and wellbeing. Failures in wastewater management may result in the release of pathogens into natural water bodies and in extreme circumstances into drinking water. Illnesses caused by contamination range from gastroenteritis and viral infections to death. As such in Australia it is a highly regulated industry accountable to a range of state authorized bodies such as Department of Environment Regulations (DER) and Department of Health (DoH) . The Shire of Moora was made responsible for their wastewater system in 2013. An analysis of this system found that the SCADA system conveyed fault location but no alarm status information. It should be noted that alarm status is needed in order to determine required responses. In order to address this problem a range of different potential solutions were evaluated according to a wide range of ranked factors such as cost, security, features etc. This resulted in the design and implementation of a low cost, distributed wireless solution based on the IEEE 802.15.4 standard. The authors believe this is the first implementation of this system in a rural/regional environment.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".