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Record W2341556438 · doi:10.2166/wqrjc.2015.045

Improving phosphorus removal of conventional septic tanks by a recirculating steel slag filter

2015· article· en· W2341556438 on OpenAlexafffund
Dominique Claveau-Mallet, Félix Lida, Yves Comeau

Bibliographic record

VenueWater Quality Research Journal · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicConstructed Wetlands for Wastewater Treatment
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSeptic tankEffluentPhosphorusEnvironmental scienceInfiltration (HVAC)WastewaterFilter (signal processing)Slag (welding)Environmental engineeringChemistryWaste managementPulp and paper industryMaterials scienceMetallurgyEngineering

Abstract

fetched live from OpenAlex

The objective of this project was to increase the phosphorus (P) retention capacity of a conventional septic tank by adding a recirculating slag filter. Two recirculation modes and recirculation ratios from 5 to 50% were tested in the laboratory with reconstituted domestic wastewater. The best system was recirculation from the end to the inlet of the second compartment of a septic tank with a 50% recirculation ratio in the slag filter, achieving 4.2 and 1.9 mg P/L at the effluent for total phosphorus (TP) and orthophosphate (o-PO4), respectively, and a pH of 8.8. The calculated size of the slag filter for a two-bedroom house application was 1,875 kg for an expected lifetime of 2 years. The 1 mg P/L level goal was not reached, but P precipitation may be favoured by the relatively high effluent pH reaching the infiltration bed.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.090
GPT teacher head0.347
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations13
Published2015
Admission routes2
Has abstractyes

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