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Record W2802458843 · doi:10.3390/w10050578

Modified Septic Tank: Innovative Onsite Wastewater Treatment System

2018· article· en· W2802458843 on OpenAlexaff
Bassim Abbassi, Raihan Abu-Harb, Bashaar Y. Ammary, Naser Almanaseer, Chris Kinsley

Bibliographic record

VenueWater · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsUniversity of OttawaUniversity of Guelph
Fundersnot available
KeywordsSeptic tankEffluentWastewaterAnaerobic exerciseEnvironmental scienceSewage treatmentWaste managementHydraulic retention timeEnvironmental engineeringEngineeringBiology

Abstract

fetched live from OpenAlex

This research documents two innovative designs of septic tanks used for onsite wastewater treatment. The designs were implemented and tested as part of a research project focused on innovative decentralized wastewater treatment solutions. The modified septic tanks were tested at different hydraulic loading rates for sufficient periods to effectively evaluate their performance. The two systems were designed with successive anaerobic and aerobic chambers and were differentiated between attached and suspended growth. The systems were operated at detention times of 4.3, 3.2, and 2.6 days. High removal of organic load was achieved under all loading criteria in both systems. Effluent BOD5 concentration at lower and higher loading rates were found to be less than 15 and 25 mg/L, respectively, representing a removal rate of more than 95%. Nitrogen was also removed but at a lower rate. The highest TN removal was achieved (59%) in the attached growth system at the lowest loading rate. Although two logs of E. coli removal (99%) were achieved in all systems, E. coli numbers were high enough to necessitate further tertiary treatment. The modified septic tanks proved to be a cost-effective technology with low energy and O&M requirements.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.051
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.016

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 head0.014
GPT teacher head0.216
Teacher spread0.202 · 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; both teacher heads agree on what is shown here.

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

Citations34
Published2018
Admission routes1
Has abstractyes

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