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Record W4404654994 · doi:10.1007/978-3-031-67739-7_17

Water Reuse in the European Union: Risk Management Approach According to the Regulation (EU) 2020/741

2024· book-chapter· en· W4404654994 on OpenAlexaff
Roberta Maffettone, Kyriakos Manoli, Pietro Drei, Caterina Cacciatori, Roberta Bellini, Bernd Manfred Gawlik

Bibliographic record

VenueLecture notes in chemistry · 2024
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsPricewaterhouseCoopers (Canada)
Fundersnot available
KeywordsEuropean unionReuseBusinessEnvironmental scienceRisk analysis (engineering)International tradeWaste managementEngineering

Abstract

fetched live from OpenAlex

Abstract In 2020, the Regulation (EU) 2020/741 on minimum requirements for water reuse in agricultural irrigation was approved with the aim to promote water reuse in the European Union. This regulation outlines minimum water quality and monitoring requirements, permitting and transparency provisions related to water reuse, as well as risk management requirements regarding the safe reuse of treated urban wastewater. The Joint Research Centre (JRC) of the European Commission developed technical guidelines based on established global approaches and European legal frameworks to assess and manage health and environmental risks associated with water reuse. These guidelines were developed with inputs from experts, EU Member State representatives, and stakeholders, and were informed by workshops, consultations, and case studies from various European Member States. This chapter presents the proposed methodology for the preparation of the risk management plan of a water reuse system where reclaimed water is used for agricultural irrigation, according to the elements listed in Annex 2 of the aforementioned regulation on water reuse.

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)0.006
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.003
Scholarly communication0.0090.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.008
GPT teacher head0.195
Teacher spread0.187 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2024
Admission routes1
Has abstractyes

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