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Record W2036747235 · doi:10.1039/b819464e

JEM Spotlight: Recent advances in analysis of pharmaceuticals in the aquatic environment

2009· review· en· W2036747235 on OpenAlex
Charles S. Wong, Sherri L. MacLeod

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Environmental Monitoring · 2009
Typereview
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsUniversity of AlbertaUniversity of Winnipeg
Fundersnot available
KeywordsEnvironmental scienceAquatic ecosystemGroundwaterPollutantEnvironmental monitoringEnvironmental planningBiochemical engineeringEnvironmental resource managementEnvironmental engineeringEnvironmental chemistryEcologyEngineeringBiologyChemistry

Abstract

fetched live from OpenAlex

Both ecosystem and human health rely on clean, abundant supplies of water, thus many classes of potential pollutants are regulated. In recent years, the possible risks associated with largely uncontrolled inputs of pharmaceuticals to rivers, lakes, groundwater, and coastal waters, mainly via wastewater, have been a focus of much research. During this time, our capacity to sequester, identify, and quantify pharmaceuticals in environmental matrices has improved. Devices have emerged to allow passive uptake of drugs to augment or replace laborious grab sampling. Advances in sample preparation have streamlined extraction procedures and removed interfering matrix components. New instrumental techniques have allowed faster, more accurate and sensitive detection of drugs in water samples. This review highlights all of these advances, from sample collection to instrumental analysis, which will continue to help us better understand the fate and effects of pharmaceuticals in aquatic systems.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.066
GPT teacher head0.384
Teacher spread0.318 · 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