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Record W4235425188 · doi:10.18280/ijsdp.160211

Considerations in the Methodology for the Technical-Environmental Viability of Sanitary Landfills in Rural Communities. Northern Case of the Province of Santa Elena, Ecuador

2021· article· en· W4235425188 on OpenAlexvenueno aff
Fernando Morante-Carballo, Boris Apolo-Masache, Paúl Carrión-Mero, Bolívar Cedeño, Javier Montalvan-Toala

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2021
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsMunicipal solid wasteEnvironmental planningRural sectorRural areaTourismRestructuringEnvironmental scienceEnvironmental impact assessmentCivil engineeringEnvironmental engineeringWaste managementGeographyEngineeringEnvironmental protectionEnvironmental resource managementBusinessEcologyPolitical scienceArchaeology

Abstract

fetched live from OpenAlex

This research focuses on providing a solution to rural sectors' problems regarding solid waste management and final disposal. It considered the Sanitary Landfill (SL) technical-environmental viability for rural communities in the north of Santa Elena-Ecuador. The objective is to propose a methodology for evaluating a sanitary landfill's technical-environmental viability, considering a Key Factors Matrix (KFM) for the possibility of its application in rural communities. The proposed methodology is based on: i) identification of preliminary and field data for assessment of the sector through the KFM, and ii) determination of the technical-environmental viability of an SL according to the aspects considered. The KFM allowed the Ayangue commune to be chosen for the location of the SL under certain precautions. Given this sector's tourist influence, solid waste accumulates 40 tons per day from the ninth year on. Therefore, it is recommended to bear in mind a possible restructuring of the SL, from a semi-mechanized system to a fully mechanized one.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.183

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 studies0.0000.000
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 head0.025
GPT teacher head0.278
Teacher spread0.253 · 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 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

Citations5
Published2021
Admission routes1
Has abstractyes

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