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Record W2923358735 · doi:10.1186/s12302-019-0193-1

Future water quality monitoring: improving the balance between exposure and toxicity assessments of real-world pollutant mixtures

2019· article· en· W2923358735 on OpenAlex

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

VenueEnvironmental Sciences Europe · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsEnvironment and Climate Change Canada
FundersFundação de Amparo à Pesquisa do Estado de São PauloEuropean Commission
KeywordsEnvironmental scienceWater qualityEnvironmental monitoringContaminationAdverse Outcome PathwayPollutantSampling (signal processing)Water Framework DirectiveBiochemical engineeringIdentification (biology)Environmental resource managementComputer scienceEcologyEnvironmental engineeringEngineeringBiology

Abstract

fetched live from OpenAlex

Environmental water quality monitoring aims to provide the data required for safeguarding the environment against adverse biological effects from multiple chemical contamination arising from anthropogenic diffuse emissions and point sources. Here, we integrate the experience of the international EU-funded project SOLUTIONS to shift the focus of water monitoring from a few legacy chemicals to complex chemical mixtures, and to identify relevant drivers of toxic effects. Monitoring serves a range of purposes, from control of chemical and ecological status compliance to safeguarding specific water uses, such as drinking water abstraction. Various water sampling techniques, chemical target, suspect and non-target analyses as well as an array of in vitro, in vivo and in situ bioanalytical methods were advanced to improve monitoring of water contamination. Major improvements for broader applicability include tailored sampling techniques, screening and identification techniques for a broader and more diverse set of chemicals, higher detection sensitivity, standardized protocols for chemical, toxicological, and ecological assessments combined with systematic evidence evaluation techniques. No single method or combination of methods is able to meet all divergent monitoring purposes. Current monitoring approaches tend to emphasize either targeted exposure or effect detection. Here, we argue that, irrespective of the specific purpose, assessment of monitoring results would benefit substantially from obtaining and linking information on the occurrence of both chemicals and potentially adverse biological effects. In this paper, we specify the information required to: (1) identify relevant contaminants, (2) assess the impact of contamination in aquatic ecosystems, or (3) quantify cause-effect relationships between contaminants and adverse effects. Specific strategies to link chemical and bioanalytical information are outlined for each of these distinct goals. These strategies have been developed and explored using case studies in the Danube and Rhine river basins as well as for rivers of the Iberian Peninsula. Current water quality assessment suffers from biases resulting from differences in approaches and associated uncertainty analyses. While exposure approaches tend to ignore data gaps (i.e., missing contaminants), effect-based approaches penalize data gaps with increased uncertainty factors. This integrated work suggests systematic ways to deal with mixture exposures and combined effects in a more balanced way, and thus provides guidance for future tailored environmental monitoring.

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)0.001
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 score0.194
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.030
GPT teacher head0.312
Teacher spread0.282 · 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