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Record W2323803698 · doi:10.1061/40927(243)135

Occurrence and Risk Management of EDCs and PPCPs in Surface Waters

2007· article· en· W2323803698 on OpenAlexaboutno aff
Glen R. Boyd

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2007 · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental impact of pharmaceuticals and personal care productsEnvironmental scienceSewage treatmentSewageEnvironmental chemistryEnvironmental planningEnvironmental engineeringChemistry

Abstract

fetched live from OpenAlex

The occurrence and risk management of endocrine disrupting chemicals (EDCs) and pharmaceuticals and personal care products (PPCPs) are topics receiving considerable attention in recent years. Monitoring of EDC and PPCP residues has been conducted in raw and treated sewage, surface waters, ground waters, and drinking waters. Recent research has been aimed at improving analytical methods and furthering knowledge of fate and transport processes, environmental risks, source reduction, and risk management including treatment of EDC and PPCP contaminants. This paper provides an overview of the topic and regulatory issues pertaining to our understanding and managing of EDCs. In addition, this paper provides discussion on emerging concerns regarding PPCPs in the environment. Completed research is described pertaining to development of analytical methods, occurrence of EDCs and PPCPs in waters of southeastern Louisiana USA and Ontario Canada, assessment of drinking water treatment processes, and experimental results regarding the formation of chlorinated naproxen byproducts and impact of these byproducts on a simulated biofilm system. And finally, EDCs and PPCPs are described from an industry perspective.

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)0.001
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueWorld Environmental and Water Resources Congress 2007Same topicPharmaceutical and Antibiotic Environmental ImpactsFrench-language works237,207