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Record W2278615190 · doi:10.1139/as-2015-0002

Characterizing phosphorus removal in passive waste stabilization ponds in Arctic communities

2016· article· en· W2278615190 on OpenAlexafffundvenueabout
Jordan J. Schmidt, Colin M. Ragush, Wendy H Krkošek, Graham A. Gagnon, Rob Jamieson

Bibliographic record

VenueArctic Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhosphorusEffluentEnvironmental scienceSewage treatmentWastewaterFjordEnvironmental engineeringChemistryOceanography

Abstract

fetched live from OpenAlex

A majority of communities in the Canadian territory of Nunavut rely on passive waste stabilization ponds (WSPs) for domestic wastewater treatment. Little research has been conducted on the treatment performance of these systems. Therefore, in response to impending federal wastewater regulations, a research program was conducted in order to characterize contaminant removal, with phosphorus a contaminant of particular concern. The performance of WSPs in the Arctic communities of Kugaaruk, Pond Inlet, Grise Fiord, and Clyde River was evaluated from 2011 to 2014. Removal of total phosphorus was highly variable, ranging from 24% (Pond Inlet, 2014) to 76% (Grise Fiord, 2011). The average removal efficiency was 44%. Effluent total phosphorus concentrations generally exceeded 7 mg P/L, partly due to elevated raw wastewater concentrations. Over the course of the treatment season (defined as June to September, when the WSP is thawed), limited additional total phosphorus removal was observed. A fractionation analysis of WSP sediments showed that organic phosphorus and phosphorus bound to aluminum and iron were the predominant forms, which provided insight into primary treatment mechanisms. Further studies on these mechanisms are needed in order to optimize Arctic WSP treatment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.013
GPT teacher head0.225
Teacher spread0.212 · 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.

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

Citations7
Published2016
Admission routes4
Has abstractyes

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