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Record W1990547473 · doi:10.1080/09593332508618416

Use of Leaching Chambers for On-Site Sewage Treatment

2002· article· en· W1990547473 on OpenAlexaffabout
J.G. St. Marseille, Bruce C. Anderson

Bibliographic record

VenueEnvironmental Technology · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsRoss Video (Canada)
Fundersnot available
KeywordsLeaching (pedology)Environmental scienceDenitrificationEnvironmental engineeringSewage treatmentWastewaterWaste managementAerationChemistrySoil waterNitrogenEngineering

Abstract

fetched live from OpenAlex

An innovative chamber system was installed for on-site sewage treatment beneath an active parking lot at a restaurant near Cornwall, Ontario. The configuration of this prototype system used polyethylene leaching chambers over which wastewater was allowed to trickle. The chambers were vented to the surface to provide direct, passive air transfer. This demonstration project was examined as a cost-effective wastewater treatment alternative for a very constrained site. The leaching chambers were installed over a geotextile-covered sand filter bed. Chamber sidewall contact contributed an additional 50% to the total soil contact area hence justification for a footprint reduction. A labile carbon source (sawdust) was added into one half of the bed to encourage dissimilatory denitrification. Average hydraulic loading was 50 l m(-1) day(-1) (5 cm day(-1)). Treatment rates exceeded more than 4 orders of magnitude removal for E. coli; 90% biochemical oxygen demand; ammonium; and 99% total phosphorus. Nitrate-N on the carbon-amended side averaged 0.6 mg l(-1) compared with 8.6 mg l(-1) on the (non-carbon) control side. This project has demonstrated that effective on-site treatment can be accomplished. Flow and load equalization, pulse dosing, chamber venting, phosphorus precipitation, and denitrification were keys to treatment success. Applications include domestic and commercial sites.

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 score0.192
Threshold uncertainty score1.000

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)0.0020.001

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.

Opus teacher head0.029
GPT teacher head0.208
Teacher spread0.179 · 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

Citations3
Published2002
Admission routes2
Has abstractyes

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