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Record W4366462992 · doi:10.1111/gwat.13316

Nitrogen and Phosphorus Treatment Can Be Sustainable During on‐Site Wastewater Disposal

2023· article· en· W4366462992 on OpenAlexaffabout
W. D. Robertson, Richard J. Elgood, Dale R. Van Stempvoort, Susan J. Brown, Sherry L. Schiff

Bibliographic record

VenueGround Water · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Waterloo
Fundersnot available
KeywordsEnvironmental scienceWastewaterEffluentDenitrificationContext (archaeology)GroundwaterEnvironmental engineeringNutrientNitrogenEnvironmental chemistryChemistryGeology

Abstract

fetched live from OpenAlex

Monitoring of a seasonal-use, on-site wastewater disposal system (septic system) in Canada, over a 33-year period from 1988 to 2021, showed that during recent sampling the groundwater plume had TIN (total inorganic nitrogen) averaging 12.2 mg/L that was not significantly different than early values, representing 80% removal, whereas SRP (soluble reactive phosphate), although higher than early values averaging 0.08 mg/L, was still 99% lower than the effluent concentration. Evidence suggests that the anammox reaction and possibly also denitrification contribute to TIN removal, whereas SRP removal is primarily the result of mineral precipitation. Most of the removal occurs in close proximity to the drainfield infiltration pipes (within about 1 m) demonstrating that reaction rates are relatively fast in the context of typical groundwater plume residence times. This long-term consistency demonstrates that sustainable nutrient treatment can be achieved with conventional on-site wastewater disposal systems that have low capital costs and require minimal energy input and maintenance.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.263

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.010
GPT teacher head0.204
Teacher spread0.194 · 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 designObservational
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

Citations0
Published2023
Admission routes2
Has abstractyes

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