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Risks of hypoxia and acidification in the high energy coastal environment near Victoria, Canada's untreated municipal sewage outfalls

2018· article· en· W2809588048 on OpenAlexafffundabout
Jeremy Krogh, Debby Ianson, Roberta C. Hamme, Christopher J. Lowe

Bibliographic record

VenueMarine Pollution Bulletin · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsFisheries and Oceans CanadaCapital Regional DistrictOcean Networks Canada SocietyUniversity of Victoria
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsOutfallHypoxia (environmental)Environmental scienceWater columnWastewaterSewageSubmarine pipelineSewage treatmentOceanographyEnvironmental engineeringWater qualityEstuaryHydrology (agriculture)Environmental chemistryOxygenGeologyEcologyChemistryBiology

Abstract

fetched live from OpenAlex

Wastewater disposal often has deleterious impacts on the receiving environment. Low dissolved oxygen levels are particularly concerning. Here, we investigate the impacts on dissolved oxygen and carbon chemistry of screened municipal wastewater in the marine waters off Victoria, Canada. We analyzed data from undersea moorings, ship-based monitoring, and remotely-operated vehicle video. We used these observations to construct a two-layer model of the nearfield receiving environment. Despite the lack of advanced treatment, dissolved oxygen levels near the outfalls were well above a 62 μmol kg−1 hypoxic threshold. Furthermore, the impact on water column oxygen at the outfall is likely <2 μmol kg−1. Dissolved inorganic carbon is not elevated and pH not depressed compared to the surrounding region. Strong tidal currents and cold, well-ventilated waters give Victoria's marine environment a high assimilative capacity for organic waste. However, declining oxygen levels offshore put water near the outfall at risk of future hypoxia.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
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)0.0020.000

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 head0.012
GPT teacher head0.201
Teacher spread0.189 · 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.

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

Citations11
Published2018
Admission routes3
Has abstractyes

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