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Record W2051646735 · doi:10.1021/es070344q

Fate of Pharmaceutical and Trace Organic Compounds in Three Septic System Plumes, Ontario, Canada

2008· article· en· W2051646735 on OpenAlexafffundabout
Cherilyn Carrara, Carol J. Ptacek, W. D. Robertson, David W. Blowes, Michael C. Moncur, Ed Sverko, Sean Backus

Bibliographic record

VenueEnvironmental Science & Technology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsUniversity of WaterlooEnvironment and Climate Change Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSeptic tankEnvironmental chemistryGroundwaterEnvironmental scienceWastewaterAnoxic watersAquiferContaminationChemistryEnvironmental engineeringGeology

Abstract

fetched live from OpenAlex

Three high volume septic systems in Ontario, Canada, were examined to assess the potential for onsite wastewatertreatment systems to release pharmaceutical compounds to the environment and to evaluate the mobility of these compounds in receiving aquifers. Wastewater samples were collected from the septic tanks, and groundwater samples were collected below and down gradient of the infiltration beds and analyzed for a suite of commonly used pharmaceutical and trace organic compounds. The septic tank samples contained elevated concentrations of several pharmaceutical compounds. Ten of the 12 compounds analyzed were detected in groundwater at one or more sites at concentrations in the low ng L(-1) to low microg L(-1) range. Large differences among the sites were observed in both the number of detections and the concentrations of the pharmaceutical compounds. Of the compounds analyzed, ibuprofen, gemfibrozil, and naproxen were observed to be transported atthe highest concentrations and greatest distances from the infiltration source areas, particularly in anoxic zones of the plumes.

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 categoriesScience and technology studies, Insufficient 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 score0.253
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
Bibliometrics0.0000.001
Science and technology studies0.0000.005
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.226
Teacher spread0.213 · 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

Citations195
Published2008
Admission routes3
Has abstractyes

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