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Record W2902023744 · doi:10.1680/jenes.17.00023

Sustainable decentralised wastewater treatment schemes in the context of Lobitos, Peru

2018· article· en· W2902023744 on OpenAlexvenueno aff
Christiana Smyrilli, Sivasakthy Selvakumaran, Michael Alderson, Alejandro Pizarro, Diego Almendrades, Britanny Harris, Andrés Bustamante

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research Council
KeywordsContext (archaeology)SustainabilityEnvironmental planningBusinessSewage treatmentEnvironmental resource managementEnvironmental scienceEnvironmental engineeringGeographyEcology

Abstract

fetched live from OpenAlex

The implementation of decentralised wastewater treatment systems, such as biodigesters, septic tanks and treatment ponds, provides opportunities for rural or remote communities to be self-reliant and avoid infrastructural connections to faraway urban areas. However, the effectiveness, sustainability and success of such systems is heavily tied to understanding the overall context (geographical, social, cultural, political and economic) in which they are installed, as well as the ease of their operation and maintenance in the long term. Shortcomings to addressing these aspects can lead to the failure of a project. Using the town of Lobitos, located in the Piura District on the northern coast of Peru, as the case study for this research, this paper is discussing and analysing the use of biodigesters as a more sustainable solution over larger municipal wastewater systems in the context of Lobitos. It explains reasons, such as community engagement, behind past failures of such systems and outlines lessons learned from a practitioner’s perspective. It concludes that addressing the local context as well as considering its impact throughout the project cycle, such as installation and future operation and maintenance, helps to ensure continued delivery of safe and sustainable wastewater treatment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.189
Teacher spread0.184 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations9
Published2018
Admission routes1
Has abstractyes

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