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Record W6980785991

Contributions from Healthcare Facilities to the overall Mass Loading of Pharmaceuticals on Wastewater Treatment Plants

2010· dissertation· en· W6980785991 on OpenAlexfundno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2010
Typedissertation
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsnot available
FundersTrent University
KeywordsNucleofectionGestational periodFusible alloyDiafiltrationProteogenomicsTSG101LiquationHyporeflexia
DOInot available

Abstract

fetched live from OpenAlex

The presence of human pharmaceuticals in the aquatic environment is now becoming a well-established fact. The identified problems associated with their presence include the fact that these compounds are biologically active, some of them are toxic in nature, and a number of compounds have potential to foster and maintain drug resistant microorganisms. They are discharged into the aquatic environment from a variety of sources, but mainly by the excretion of incompletely metabolized pharmaceuticals by individuals into the wastewater. This situation makes finding a source-control strategy difficult. However, healthcare facility (hospitals and long-term-care homes) effluents are suspected to have relatively higher concentrations of these compounds, as such facilities use pharmaceuticals in large amounts for diagnostic, cure and research purposes. It is expected that controlling discharges from these facilities may provide a cost-effective solution to reduce the pharmaceutical loads entering the aquatic environment.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.024
GPT teacher head0.268
Teacher spread0.243 · 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
Published2010
Admission routes1
Has abstractyes

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