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Record W4319320493 · doi:10.5772/intechopen.109928

Treatment Technologies and Guidelines Set for Water Reuse

2023· book-chapter· en· W4319320493 on OpenAlexaboutno aff
Ahmed Abou-Shady, Heba El-Araby

Bibliographic record

VenueSustainable development · 2023
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsnot available
FundersScience and Technology Development Fund
KeywordsReuseAgricultureReclaimed waterEnvironmental planningBusinessPopulationWater resourcesEnvironmental scienceWater resource managementWater conservationNatural resource economicsEngineeringWaste managementGeography

Abstract

fetched live from OpenAlex

Water reuse is considered a practice that is currently embraced worldwide owing to the exacerbated water crisis, which is the result of several factors such as the increasing world population, urbanization, industrial sector, global climate change, limited water resources, and agricultural activities. Water reuse is not used intensively only in arid and semi-arid regions, which are characterized by limited water supply but can also be applied in countries that possess sufficient water resources (e.g., Brazil and Canada are implementing policies for water reuse). This chapter discusses the treatment technologies proposed for water reuse and presents some recent guidelines set for water reuse. Treatment technologies typically have three main processes: primary, secondary, and tertiary. There are several set guidelines worldwide for water reuse, however, a universal standard guideline to facilitate the reuse of reclaimed water has not been established. No federal regulations for reusing recycled water have been established in the United States; however, several individual states and territories have established specific regulations to manage reclaimed water for various purposes, including agricultural irrigation, animal watering, and crop production.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.394
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.035
GPT teacher head0.253
Teacher spread0.217 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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