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Record W2609298324 · doi:10.5539/mas.v11n6p41

Wastewater Automation – The Development of a Low Cost, Distributed Automation System

2017· article· en· W2609298324 on OpenAlexvenueno aff
Stanislaw Maj

Bibliographic record

VenueModern Applied Science · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsWastewaterSCADAALARMAutomationBusinessComputer scienceComputer securityEnvironmental scienceEnvironmental engineeringEngineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.260
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2017
Admission routes1
Has abstractyes

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